Computer vision system and method for automatic checkout

ABSTRACT

A system and method for computer vision driven applications in an environment that can include collecting image data across an environment; maintaining an environmental object graph from the image data whereby maintaining the environmental object graph is an iterative process that includes: classifying objects, tracking object locations, detecting interaction events, instantiating object associations in the environmental object graph, and updating the environmental object graph by propagating change in at least one object instance across object associations; and inspecting object state for at least one object instance in the environmental object graph and executing an action associated with the object state. The system and method can be applied to automatic checkout, inventory management, and/or other system integrations.

CROSS-REFERENCE TO RELATED APPLICATIONS

This Application is Continuation Application of U.S. patent applicationSer. No. 16/803,229, filed on 27 Feb. 2020, which is a ContinuationApplication of U.S. patent application Ser. No. 16/416,009, filed on 17May 2019, and granted as U.S. Pat. No. 10,614,514, which is aContinuation Application of U.S. patent application Ser. No. 15/590,467,filed on 9 May 2017, and granted as U.S. Pat. No. 10,339,595, whichclaims the benefit of U.S. Provisional Application No. 62/333,668, filedon 9 May 2016, all of which are incorporated in their entirety by thisreference.

TECHNICAL FIELD

This invention relates generally to the field of commerce and inventorymanagement, and more specifically to a new and useful system and methodfor computer vision driven applications within an environment.

BACKGROUND

Stores and warehouses are generally confronted with the challenge ofmanaging inventory. From an operational perspective, many sophisticatedbusiness operations devote considerable time and resources to manualcounting of inventory. Such a process is prone to errors and onlyprovides periodic views into the state of inventory.

In connection with inventory management with respect to customer facingaspects, stores are very dependent on traditional tools and systems forcharging customers for their products and/or services. Traditional pointof sale systems and even self-checkout options commonly result in longlines, which can leave customers dissatisfied with their shoppingexperience. This can be particularly damaging when physical shopping isthreatened by online shopping options.

Thus, there is a need in the commerce and inventory management field tocreate a new and useful system and method for computer vision drivenapplications within an environment. This invention provides such a newand useful system and method.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a schematic representation of a system of a preferredembodiment;

FIG. 2 is a schematic representation of an exemplary system installationof a preferred embodiment;

FIG. 3 is a schematic representation of an exemplary scenario where theEOG is applied for applications in different regions of the environment;

FIG. 4 is a schematic representation of a single image capture deviceused in implementing the system and method;

FIGS. 5-8 are schematic representations of exemplary image capturedevice configurations of the system;

FIG. 9 is a schematic representation of interaction modeling for anexemplary path of a shopper;

FIG. 10 is a schematic representation of object transformations;

FIG. 11 is a flowchart representation of a method of a preferredembodiment;

FIG. 12 is a schematic representation of exemplary integrations withsupplementary data inputs;

FIG. 13 is a schematic representation of iterative processes involved inmaintaining the EOG;

FIG. 14 is an exemplary schematic of object classifications and compoundobject modeling with item count estimations;

FIGS. 15A and 15B are schematic representations of object paths andassociating different object path segments at termination points of theobject paths;

FIG. 16 is a schematic representation of propagating updated objectstate across a motion path to alter previous object classifications;

FIGS. 17A and 17B are schematic representations of exemplary scenariosinvolving compound object modeling and interaction events;

FIG. 18 is a schematic representation of an exemplary sequence involvingcompound object modeling and multi-state modeling with interactionevents;

FIGS. 19A and 19B are schematic representations of interactions betweendifferent objects using compound object modeling and probabilisticmulti-state modeling, and in FIG. 19B propagating an object state updateand resolving the multi-state modeling of FIG. 19A;

FIGS. 20 and 21 are schematic representations of updates to an EOG;

FIG. 22 is a is a flowchart representation of a method applied toautomatic checkout;

FIG. 23 is a is a flowchart representation of a method applied tointegrating with a checkout processing system;

FIG. 24 is a schematic representation of communicating the checkoutlist;

FIG. 25 is a schematic representation of resolving shopper associatedobjects to a checkout list;

FIG. 26 is a schematic representation of a calibration event;

FIG. 27 is a schematic representation of storing media according to theEOG in a meta-media storage format;

FIG. 28 is a flowchart representation of the method applied togenerating a checkout list; and

FIGS. 29-35 are schematic representations of exemplary process instanceswhen maintaining the EOG.

DESCRIPTION OF THE EMBODIMENTS

The following description of the embodiments of the invention is notintended to limit the invention to these embodiments but rather toenable a person skilled in the art to make and use this invention.

1. Overview

A system and method for computer vision (CV) driven applications withinan environment of a preferred embodiment function to seamlessly monitor,track, and account for objects within an observed space. The system andmethod preferably utilize an imaging system and processing engine thataccounts for object state through a statistically based approach. Thesystem and method described herein can transform the operationalcapabilities of surveillance systems and enable new integrations andapplications with other systems as shown in FIG. 1.

The system and method are preferably resilient to imperfect inputsources and unknown states and can preferably apply longitudinalmodeling to self-correct with new image data across time and space. Thesystem and method can be particularly applied to CV-driven applicationsthat depend on tracking of multiple objects (e.g., products) alongsidepeople or device objects (e.g., shoppers, workers, carts, bags,) whereinteractions and movement of objects is of interest. The system andmethod can operate under imperfect input sources and updateunderstanding with new information.

Preferably, the system and method function to monitor object-to-objectassociations. Object-to-object associations can relate to one objectpossessing or containing another object or possibly being composed ofthat object. Through object-to-object associations, the state of anobject can be tracked across time and space without direct confirmableobservable visual data at a different instance of time and space. Forexample, a person could pick up an item in a store, place that item inhis pocket, and the system and method can account for that itempossessed by the person when the person leaves the store.

The system and method can use computer vision detection at variouspoints and at different times to update an understanding of objectcontents, associations, and/or presence within an environment. Inaddition to accounting for visually identifiable products, unclearvisual observations and visually obscured objects may be accounted forthrough periodic updates and propagation of modeling through object andtemporal relationships such as the object-to object associationsmentioned above. This sort of virtual representation of objects in thephysical world can include modeling objects such as inventory items,workers, shoppers, containers, storage elements (e.g., shelves), foreignobjects, and/or any suitable type of object.

The modeling applied by the system and method can be characterizedthrough a modeling system referred herein as an environmental objectgraph (EOG) that reflects modeled object state and associationsthroughout time.

The system and method can have applications in a wide variety ofenvironments. In one variation, the system and method can be used withinan open environment such as a shopping area or sales floor whereinventory interactions are unstructured. Shoppers may interact withinventory in a variety of manners, which may involve product inspection,product displacement, adding items to carts or bags, and/or otherinteractions. For example, the system and method can be used within astore such as a grocery store, big box store, bookstore, conveniencestore, drugstore, shoe store, and/or any suitable type of shoppingenvironment. When used within a shopping environment, the system andmethod may facilitate an alternative checkout process, which may befaster and in some cases automatic.

The system and method may alternatively be used within a structuredindustrial environment, where interactions with products are moreconstrained and largely limited to prescribed worker interactions. Someindustrial environments may be primarily characterized by structuredentity-inventory interactions. An entity can be a person or an automatedsystem such as robotic item-mover. People and robots may both be used inadding or removing items to the environment or moving the items. Forexample, the system and/or method can be used in a warehouse, storagefacility, store backroom, cabinets, manufacturing environment, and/orany suitable industrial environment.

The system and method may alternatively be used within other types ofenvironments or application such as within a private home to trackpersonal possessions.

The system and method are preferably used in combination with an imagingsystem that includes a distributed network of imaging devices as shownin FIG. 2. That imaging system can be installed in a store or isolatedenvironment. For example, the system and method could be deployed withina store environment to monitor inventory and shoppers in a storefrontand to monitor inventory stored in a storage and loading area. Differentinstantiations of the system and method can be used in connection with anetwork accessible computing platform such that multiple stores orlocations at different sites can operate cooperatively, providehigher-level insights to chain managers, and/or leverage data insightsacross different locations.

The system and method could additionally be implemented in a widerdistributed system, wherein operational logic of the system and methodcan be executed across one environment with a network of imagingdevices. Different CV-based applications can be executed withindifferent continuous or discontinuous regions. This can enableregionally specific CV-driven applications that can open upinternal-region CV-based applications and cross-region CV-basedapplications. This could be used in a mall-like environment wheremultiple stores use the system and method. The system and method at themall may enable store-specific CV-driven applications like automaticcheckout within each store region, but could also enable inter-store ormall level interactions as shown in FIG. 3.

The system and method could similarly be applied at a smaller scale,wherein the system and method could work with a single camera thatapplies EOG modeling across different regions within the field-of-viewof the camera. In one implementation, object interactions by a customerat one location can be applied to system operation when the customerenters another region. For example, a small shelf of items could bemonitored such that item selection by a shopper can be tracked and thenan automatic checkout process can execute when the shopper leaves thefield of view or proceeds through an exit as shown in FIG. 4.

The system and method can have a wide variety of applications and beused in combination with other components. As one primary application,the system and method can be applied for automatic self-checkout.Automatic self-checkout is primarily characterized by a system or methodthat generates or maintains a virtual cart (i.e., a checkout list)during the shopping process with the objective of knowing the possesseditems when a customer leaves a store. The system and method can be usedto automatically charge an account of a customer for the total of avirtual cart or to generate an itemized summary that can be communicatedto a checkout processing system (e.g., a point of sale (POS) device or acustomer application on a personal computing device) for direct payment(e.g., paying via cash, credit/debit card, etc.). In some cases, thesystem and method may be used in assisted checkout wherein a virtualcart or monitored information of a shopper can be used in augmenting acheckout processing system.

The system and method may alternatively be used to account for theremoval of an item by a customer such as in a library, a rental store, awarehouse, or any suitable item storage facility. The system and methodmay alternatively be used to permit or restrict access to locations orto charge for such access such as at an airport, an amusement park, or agym. The system can be made to work for a wide variety of shoppingenvironments such as grocery stores, convenience stores, micro-commerce& unstaffed stores, bulk-item stores, pharmacies, bookstores,warehouses, malls, markets, cafeterias, and/or any suitable environmentthat promotes commerce or exchange of goods or services. Herein,automatic self-checkout for an accumulated set of items is used as amain exemplary application of the system and method, but any suitableapplication may be used.

As an exemplary scenario of the system and method in practice, thesystem and method may detect a shopper adding boxes from the cerealaisle into the shopping cart. The number of boxes within the cart maynot be observable because of the current view of the camera. The systemand method, however, can update the internal representation of inventoryin the EOG based on some interaction which has some probabilisticoutcome based on various factors (e.g., location of the shopper in thestore). The system and method may for example update its understandingto predict with 90% probability that a shelf holds 9 boxes, an 8%probability the shelf has 8 boxes, and a 2% probability of holding 7 orfewer. A shopper's cart similarly can be assigned a probabilisticdistribution of having some set of contents. At some future point, theshopper's cart may be observed to contain three visible cereal boxes.Such information is used to update the inventory representation in theEOG such that the shopping cart contents is predicted with a 80%probability to have three boxes and a 20% probability to have four ormore boxes. The shelf inventory count is similarly adjusted. Otherfuture observations can similarly be used to update the inventoryrepresentation further. Other object inputs such as POS checkouts or amanual inventory counts can also be used to update the probability. Thesystem and method preferably use dynamic, machine intelligence thatactively improves the quality of the system's internal representation ofthe physical world based on feedback from various inputs.

The system and method can additionally or alternatively be applicable toinventory management. Traditionally, inventory management uses incomingshipments, checkout processing systems, itemized receipts, and periodicmanual inventory accounting to maintain a count-based understanding ofinventory. However, such a traditional approach can be rigidlyrestricted in its understanding of the inventory. For example, suchtraditional systems cannot account for product “shrinkage” (e.g.,discrepancies between expected item count and actual count oftenresulting from theft, product misplacement, or incorrect inventoryaccounting). In general, the traditional inventory management systemsalso may not provide reliable information of items while in the store,and instead depend on time consuming and manual inventory processeswhich often involve employees counting all the items in the store. Thesystem and method can preferably provide a real-time understanding ofinventory status that includes not only count expectations, but mayadditionally provide item location, item interaction history, itemshelf-life monitoring, media, metadata, and/or other information.

The system and method may additionally or alternatively be applicable tosurveillance used in security, auditing, analytics and/or other uses ofvisual monitoring. Interactions with the media, storage of the media andmedia information, and/or responding to content of media may beaddressed through implementations of the system and method.

The system and method could additionally or alternatively be applicableto triggering alerts and/or directing workers or systems within theenvironment. In some variations, customized object rules may beconfigured to trigger performance of various tasks. For example, aworker alert could be triggered when a refrigerated object is removedand left on a shelf outside of refrigeration.

As a related extension, the system and method may be implemented forcustomized operational logic for particular/custom applications and/orbe integrated with an outside system. For example, programmaticinterfaces could be enabled so that other systems can leverage theobject modeling capabilities of the system and method. The system andmethod could similarly be used for alternative applications.

The system and method can bring about a variety of potential benefits.As one potential benefit, the system and method may provide monitoringcapabilities robust to messy environments. The system and method canaccount for unstructured and unexpected object interactions. The systemand method preferably utilizes various approaches to be self-correctingand account for scenarios of imperfect detection. In association withthis potential benefit, the system and method can avoid dependence onvisual detection of complex object interactions, which can be fallibleand may not be robust in regular usage. While, the system and method mayuse interaction detection approaches in some implementations whenpossible, the system and method can preferably correct when suchdetection fails or is not directly observable. As a system and methodthat can be generalized to various CV algorithmic approaches, thearchitecture of the system and method can be implemented without beingrestricted to a particular CV-algorithm, and can seamlessly improve withthe advances of the industry.

As another related potential benefit, the system and method can addressobservational holes. Obstructions, unclear views, and hiding of objectswithin other objects are all factors that have prevented existingapproaches from providing robust object tracking. The system and methodcan operate under such situations by employing various approaches suchas indirect detection, propagation of object modeling, and/orcounterfactual predictions.

As another potential benefit, the system and method can accommodate avariety of surveillance system infrastructures. The system and methodcan be used with a wide variety of surveillance systems that may haveany number of imaging devices and quality of the imaging devices. Insome cases, a limited number of budget imaging devices may be used as alow cost solution, including an implementation with a single camera. Inother cases, the environment may have a network of various imagingdevices providing image data from various angles. In some cases,existing surveillance systems may be utilized in implementing thesystem. In still others, a combination of visual, infrared, lidar,laser, radar, sonar, acoustical sensors, electromagnetic signal mappingsystems, and other sensors may be used. Related to this, the system andmethod can preferably operate without depending on precise and timeconsuming calibration of the imaging devices.

As another potential benefit that can be used in automatic self-checkoutand other applications, the system and method can enable detection andmonitoring of shopper interactions with products throughout a shoppervisit. The shopping path, the order that items were added to a cart, thetime for selecting an item from a shelf (e.g., shelf inspection time),the final contents of a cart when leaving, and/or other factors can bedetectable through the system and method. In one variation, the systemcan be used in automating parts of the checkout process. For example, acustomer or worker checkout kiosk or other checkout processing systemcan use the system and method for enhanced functionality. In anothervariation, the system can be used in verifying products are not removedfrom the store without payment.

As one potential benefit the system and method can address inventoryaccounting. Stores and managers can more efficiently know the inventoryranges of products. Product ordering, monitoring of inventory longevityor “health” (e.g., duration of storage, monitoring expiration dates ofindividual items), and/or processing of inventory can also befacilitated through the system and method.

As a related potential benefit, the system and method can be used intracking items within a store such that the location of an item can bequeried. Additionally, the system and method may be used in monitoringobject relationships. Object relationships can include associations andinteractions such as what object was used to store another object, thecontact/proximity of particular objects, and/or other objectrelationships. Policy may be set to trigger alerts or other actions forparticular conditions. For example, if an ice cream carton is removedfrom the freezer and then left on the shelf, an alert could be generatedbecause the ice cream carton was detected to be outside of the freezerand stationary for a set amount of time.

As another potential benefit, the system and method can provide adata-driven surveillance approach, which may be used in place of, or toassist, a visual surveillance system. A data-driven surveillanceapproach may have benefits that enable a greater time frame of storedsurveillance and/or selectively prioritizing media quality in particularsituations.

The system and method can similarly expose benefits in otherapplications by elevating detailed object monitoring within anenvironment, which can be leveraged for a variety of applications.

Environmental Object Graph Modeling Overview

The system and method preferably applies an environmental object graph(EOG) modeling system, which can be used to probabilistically detect,track, and model objects in space and time across an environment.

An EOG generally describes an approach that represents physical items inan environment as objects and maintains that virtual representation asitems are moved, added, removed, and changed. The EOG is preferably adata representation structure of classified objects, wherein instancesof objects in the EOG can have various probabilistic objectassociations. The object representations in the EOG are continuously orperiodically updated according to a flow of new information. Newinformation can originate through newly classified objects, interactionsbetween objects, tracking of objects, and/or other inputs such as asupplemental data inputs like transaction data from a checkoutprocessing system.

The EOG can be particularly applicable in combination with anenvironment that has ubiquitous imaging system. The EOG system can usemultiple image-based observations from various vantage points and/or atdifferent instances to build a representation of objects and theobjects' relationships in the environment.

Probabilistic object modeling can be applied within an EOG for compoundobject modeling, probabilistic modeling of multiple states, and/orpropagating of modeling through the EOG.

In compound object modeling, the EOG system preferably enablespredicting or modeling probabilities of contained objects. When combinedwith interaction event detection this can be used in accounting for aperceived appearance of an object, disappearance of an object, splittingof an object, or merging of objects.

Probabilistic modeling of multiple states facilitates maintainingmultiple object states of potential realities. For example, an objectmay be modeled as having been selected by a shopper with some likelihoodand having been pushed deeper into a shelf so as not to be observed. Theprobabilistic modeling can be updated through updated information tomore confidently identify most likely state, and/or resolve the modelinginto a predicted state when requested for application operations.

In propagation of modeling, new information such as updated objectclassification information at a different point in time and/or from adifferent camera can be propagated across associated objects. Modelpropagation can be used to update object classifications, objecttracking, object associations, compound object modeling, and/or multiplestate modeling.

In one implementation, an object's representation within an EOG caninclude a classification and possibly a set of contained or associatedobjects. The classification may be as a SKU (stock keeping unit orglobally unique identifier), a general object (e.g., apple box, shopper,shelf, stack of goods), or an as-yet unknown object. The classificationwill generally be accompanied by a confidence level and possibly a setof other classification options. In one variation, a modeled set ofcontained objects may include any number of objects including zero. Thecontained objects are represented as being part of the contents withsome probability. A contained object may, in turn, have further objectscontained within it, and so on, recursively.

An object may additionally include or be associated other properties.For example, an object with a SKU classification may be associated withan inventory item having a price, name, weight, size dimensions, and/orother properties. In a commerce application, the object may includebusiness operational properties such as product popularity, relatedproducts, margin/profit, manufacturer/distributor, sales priority,and/or other operational related properties. When used within anenvironment wherein inventory is tracked, the EOG can be used to predictinventory status. The inventory status can include count, location,object associations (e.g., an item is inside a particular box),inventory history, and/or other properties. The EOG can additionally beused to predict items picked up by shoppers or workers, storagetechniques of objects, and/or other information.

As mentioned, an object can be classified as a SKU object, which mightcorrespond with an inventory item. A SKU object is preferablycharacterized as relating to a single item with a specific identity.That identity is preferably associated with a product (e.g., a productobject), which may include properties such as price, dimensions, weight,product media (e.g., history of product photos used in classification),and other properties. Herein, an item may generally refer to thephysical product, artifact, or manifestation that corresponds to anobject of the EOG unless otherwise specified. A SKU object can besubstantially indivisible. Herein, indivisibility is generally withinthe context of the environment. For example, a particular cereal box maybe considered indivisible within the context of a store even though inthe real world it is generally composed of a box and individual cerealpieces; still, it is stored and sold as one box. In some scenarios, aSKU object may be divisible within the EOG (e.g., a six pack of drinks),sometimes yielding differently characterized objects.

The EOG may additionally include other types of object classifications,which can be associated with specialized properties. Other types ofobject within an environment might include shoppers, workers, shoppingcarts, storage devices (e.g., shelves), product moving devices (e.g.,forklifts), and/or other suitable objects.

2. System

As shown in FIG. 1, a system of a preferred embodiment can include animaging system 100 and an EOG system 200. The system primarily functionsto track and account for physical items within an environment. The EOGsystem functions to use image-based object detection in building acomputer-based representation of the contents of an environment. The EOGsystem can use a variety of approaches in building its representation ofobjects. The system may additionally include or be used in combinationwith a virtual cart management system, a checkout processing system, aninventory management system, and/or other supplementary systemcomponents. While, the system is preferably used in combination with animaging system 100, the system can similarly be the EOG system 200driven by image data from an outside imaging system.

Imaging System

The imaging system 100 functions to collect media within the environmentas shown in FIG. 2. The imaging system 100 preferably includes a set ofimage capture devices 110 (e.g., digital cameras). An image capturedevice 100 might collect some combination of visual, infrared,depth-based, lidar, radar, sonar, and/or other types of imagery. Invariation, the imaging system 100 is a multitude of image capturedevices 110 distributed in the environment with the image capturedevices 110 positioned at a range of distinct vantage points.Preferably, the imaging system 100 is a high density imaging system. Ahigh density imaging system is preferably characterized by a largeportion of the relevant portions of environment normally observed by animage capture device. A large portion, in one example, can becharacterized as greater than 95% of surface area of interest. Highdensity may additionally be characterized as having redundant coverage.In one example, high density imaging system may be characterized by onecamera for every one hundred square feet of surface area of interest(e.g., the ground, product storage faces, etc.). In an environment likea small grocery store this can be twenty cameras distributed forcoverage of two thousand square feet of surface area of interest. Theimaging device 120 to environment space ratio could be any suitableratio. In an alternative variation, the imaging system 100 may includeonly a single image capture device.

The image data is preferably video but can alternatively be a set ofperiodic static images. In one implementation, the imaging system 100may collect image data from existing surveillance or video systems. Theimage capture devices 110 may be permanently situated in fixedlocations. Alternatively, the image capture device no or a subset of theimage capture devices may be moved, panned, zoomed, or carriedthroughout the facility in order to acquire more varied perspectiveviews.

The system can accommodate a wide variety of imaging system 100 setups,and the system can be robust while using inexpensive commodity imagecapture devices no. An image capture device 110 can be set to an imagingcapture configuration. In one implementation, the imaging system 100 mayinclude a set of image capture device 110 in different imaging captureconfigurations. The various imaging capture configurations function toallow different systems to focus on providing a view into particulartypes of object information. For example, the set of imaging captureconfigurations may include an inventory storage capture configuration,an interaction capture configuration, an object identification captureconfiguration, and/or a movable capture configuration. The variousconfigurations may involve use of a particular type of image capturedevice 110 and/or a type of mounting of the image capture device.

An inventory storage capture configuration functions to view inventoryin their expected storage locations. An inventory storage captureconfiguration can include a camera directed at a shelf or storagesolution that holds inventory items as shown in FIG. 5. Generally, imagedata from an image capture device in an inventory storage captureconfiguration will have a substantial portion of the field of viewdirected at stored inventory. The inventory storage captureconfiguration can be used in detecting changes to objects on the shelfbefore, during, and after an interaction. A variety of inventory storagecapture configurations may be used. For example, overhead inventorystorage capture configuration may have an image capture device directeddiagonally down towards a shelf. In another example, a direct inventorystorage capture configuration can be an image capture device directedsubstantially at a product storage area, such as being mounted across anaisle. In another variation, an inventory storage capture configurationcan include an internal inventory storage capture configuration whereinan image capture device is positioned so as to have an internal view ofa storage element. For example, image capture device can be positionedon the inside of shelves to view products from the back of shelves.

An interaction capture configuration may function by detecting andcharacterizing interactions between objects. In particular, theinteraction configuration may be configured for detecting andcharacterizing interactions between a person and a shelf of objects asshown in FIG. 6. An interaction configuration can detect the nature ofinteraction between a person and a shelf. In a basic implementation, theinteraction capture configuration detects if a person breaks a definedplane around products such as the front plane of a shelf to interactwith objects on the shelf. The interaction capture configuration mayinclude a visual video camera that is directed in a directionsubstantially coplanar with the face of a shelf so as to show how aperson interacts with an inventory storage compound. For example, in astore, a camera in an interaction capture configuration may be directeddown an aisle. A series of imaging devices can be positionedperiodically down an aisle, wherein each image capture device isintended for detecting interaction events with subsections of the aisle.

An object identification capture configuration may be used in uniquelyidentifying an object as a particular item. Such object identificationcapture configurations can be customized for various scenarios. Aperson-focused variation may be used for inspecting person-basedobjects. In a store, the person-focused capture configuration mayinvolve an image capture device directed down from above so as to detectitems in the shopping cart of a shopper as shown in FIG. 7. In anothervariation, a person-focused configuration may be mounted at head-height(e.g., greater than three feet and less than nine feet) and used inidentifying people based on their facial features. A checkout-focusedvariation can function to identify objects during a checkout process.The checkout process may involve more clear presentation of items suchas a shopper or worker physically presenting the product in aninspection region. For example, a user may be required to place items ona conveyer belt. The checkout variation may include one or more imagingdevices for detecting objects and updating the EOG based on checkoutinteractions. Another type of configuration may involve using a highdefinition camera and an actuating mount so that the imaging device cantarget particular objects or locations to collect data.

A movable capture configuration may be used to provide image data fromdynamic location and/or orientations. A movable capture configurationcan be used to adapt to dynamic situations. For example, one or moreimaging systems 100 may be mounted to a robot or person as shown in FIG.8, and collect image data as the imaging device is moved through theenvironment. The movable configuration may be used in collecting higherresolution or alternative, or otherwise obstructed vantage points. Amovable capture configuration can additionally be used as a calibrationtool in collecting image data used in resolving modeling issues.

In common use, most capture configurations shall be blended use,supplying information on more than one of the described situations.

Image data from an image capture device 110 is preferably streamed orotherwise communicated to a processing component of the EOG system 200.

EOG System

The EOG system 200 functions to generate, execute and/or otherwisemaintain an EOG model representation. The EOG system 200 is preferably acombination of operational processes used in maintaining the EOG anddata systems in storing and interacting with the model. Various datadatabase and modeling implementations may be used in implementing theEOG system 200. The EOG system 200 preferably uses an EOG processingengine 210 to manage an EOG data system. The EOG preferably has animaging system interface for receiving data from the imaging system 100and/or external image data sources (e.g., an existing surveillancesystem). The EOG system 200 could additionally include one or moresupplemental data interfaces, which can be used in collecting data fromor updating data services. Exemplary supplementary data inputs caninclude purchase pattern data, customer purchase history, productpricing data, inventory data (e.g., shipment orders, inventorymaps/planograms, etc.), purchase transaction data, and/or other datainputs. Additionally, the EOG system 200 can include an inspection orquery interface that facilitates interacting with the EOG. Theinspection interface can be used for integrating other systems such as acheckout processing system or an inventory management system. Theinspection interface can be used in making simple or complex queries oranalysis of the EOG, which can enable acting in response to object statein the EOG.

The EOG system 200 is preferably operable within a computing system. Inone variation, the computing system is an on-premise computing solutionsuch as an application running on a computer connected to the visionsystem. In another variation, the computing system is a remote computingsolution, wherein part or all of the processing is performed at a remotelocation. For example, a cloud or distributed computing system may beused. Alternately, an on-premise system may do a portion of thecomputational work, such as collecting media streams from the visionsystem, while the remaining computational work is performed at a remotelocation. More preferably, the EOG system 200 is integrated with theimage capture devices 110 of the imaging system 100 wherein the processof maintaining the EOG is distributed across imaging devices, which canfunction to alleviate data communication and/or storage limitations orchallenges.

The EOG processing engine 210 uses input from a computer vision systemas well as other data inputs such as inbound/outbound product orders,itemized POS checkout reports, manual inventory inputs, and/or otherobject inputs. Supplemental data inputs, which can relate to operationalaspects of the environment, can be used in altering classification andmodeling in the EOG and/or in inspecting or interpreting an EOG.

The various inputs are processed by the EOG processing engine 210 andused in storing information or updating EOG representations stored inthe EOG data system. The EOG system 200 can characterize compoundobjects in a hierarchical order. Objects can have parent and childrelationships. The parent of an object is generally the object thatcontains it. The child of an object is an object that it contains. Forexample, a box containing apples will have the box as the parent objectand the apples as the child objects. The EOG can additionally representhistorical data associated with an object relationship. For example,multiple shoppers may each remove items from a box. The items possessedby the shoppers may be unknown at the time. The subsequentidentification of an apple with one shopper may be used to update therepresentation of the box from which the apple was contained, which canthen be used to update the understanding of those items from the samebox as apples possessed by the other shoppers. In this example,propagation of information through object relationships can similarly beused to update the representation of the crate from which the boxoriginated and other aspects within the EOG system 200.

In the process of maintaining an EOG, the EOG system 200 can use avariety of approaches to draw conclusions about the state of items(e.g., inventory) in an environment. In one implementation, the EOGsystem 200 can draw information updates through processes such a staticprocess, an interaction process, and/or an associative propagationprocess.

The EOG processing engine 210 preferably implements a static processwherein data from a given instance is used in part to update the EOG'srepresentation of the objects in that instance. Static processes canrelate to object segmentation, classification, object tracking, and/orother algorithmically driven conclusions of the image data. The staticprocess can be a real-time computer vision analysis of the data inputs,which uses the available information without necessarily accounting forhistorical based object associations of the EOG. A static processpreferably uses the current image data from the computer vision systemor imaging system. In one variation of a static process, computer visionand other approaches may be used in identifying objects. Theclassification of an object may include SKU object identification,unique shopper identification, general object classification, orunidentified object classification.

Classification can leverage use of image feature extraction andclassification, statistical machine learning, neural networks,heuristical processes and other suitable approaches. In one variation,image feature extraction and classification can be used in classifyingmultiple objects present in image data, which may use processes likevisual words, constellation of feature classification, and bag-of-wordsclassification processes. These and other classification techniques caninclude use of scale-invariant feature transform (SIFT), speeded uprobust features (SURF), various feature extraction techniques cascadeclassifiers, Naive-Bayes, support vector machines, and/or other suitabletechniques. In another variation, Neural networks or CNNS such as Fastregional-CNN (r-CNN), Faster R-CNN, Mask R-CNN, and/or other neuralnetwork variations and implementations can be executed as computervision driven object classification processes.

Tracking of an object can similarly be a process operating on raw imagedata. Object tracking can use various techniques such as optical flow,algorithmic target locking and target re-acquisition, data-driveninferences, heuristical processes, and/or other suitable object trackingapproaches. As discussed herein, the EOG can additionally be used tosupplement or facilitate classification and tracking of an object.

Static processes can additionally be used in collecting or generatingcontextual information. Contextual information relating to the objectclassification may be used in building a representation of that object.Contextual information can include the location of the object or imagingdevice that captured the image, the time of day, seeded or existinginventory information or rules, and/or other types of contextualinformation. For example, a box identified as an apple crate that isdetected in the warehousing section of a grocery store around the timeof expected shipment delivery may initially be presumed with moderatelyhigh likelihood to contain a full box of apples (e.g., 40 apples), andrepresented as such.

The EOG processing engine 210 can additionally include an interactionprocess, which functions to draw conclusions on the state of items in anenvironment based on object interactions. An interaction process caninvolve two or more objects, which generally is associated withproximity, contact, and/or changes in objects in some region betweenobjects. For example, the representation of two objects can be updatedwhen the objects make contact or are moved to be within a particularproximity. As shown in FIG. 9, the path of a shopper through the storecan alter the EOG of the shopper object based on proximity to particularobjects in the store. The interaction process may include a variety ofobject transformations. Object transformations generally show thetransition of a first set of objects into a second set of objects. Aninteraction process can additionally involve a change in classification,identity, or probabilistic representation of one or both objects. Asshown in FIG. 10, examples of basic object transformations related tointeraction events may include revealing contents of an object,concealing an object by another object, splitting an object into morethan one object, merging a set of objects together, appearance of anobject, and disappearance of an object.

The EOG processing engine 210 may additionally have an associativepropagation process, which functions to draw conclusions on the state ofitems in the environment based on observations over a history ofobservations. The propagation process generally relies on inputcollected over a longer period of time in updating the EOG than theinteraction or static processes. In a propagation process, updates toone part of the EOG can propagate through historical object associationsand relationships to update a second part of the EOG at one or moreother instances in time or space. The propagation process is preferablyused in combination with the static process and the interaction process.

As a general example of a propagation process, a box may initially becharacterized (incorrectly) with low probability as an apple box and istherefore presumed to contain apples. A first shopper is seen removingan item, but that item is not clearly identified; so, that first shopperis initially updated (incorrectly) as having removed an apple and addedit to a shopping cart. A second shopper is subsequently seen removingsome object from the same box, but that object is classified with muchhigher probability as a grapefruit. The box in the EOG may be updated tobe more likely to contain grapefruit. Additionally, the first shopper inthe EOG may be updated to also possess (e.g., modeled as “containing”) agrapefruit through the propagation process of the EOG system 200.Mistakes can be made in the initial static process, but they arepreferably well tolerated by the systems because that can be correctedlater. In this way the represented quality of the entire EOG activelycan evolve to be more accurate over time. Since there are many moreopportunities to correct a mistake than to get it right the first time,the overall EOG system 200 functions with a very high certainty.

The system can additionally include a reference object database thatfunctions to provide object property information. Object propertyinformation may in some variations be used in modeling objects throughthe EOG system 200. Object property information may alternatively oradditionally be used in applying the modeled objects in someapplication.

A reference object database preferably includes at least an inventoryreference database. The inventory database preferably includes productor SKU information and properties such as item physical properties, itembusiness properties such as price, purchase popularity, related items,and/or other information. A set of image training samples of productscan additionally be part of the reference object database.

A reference object database can additionally include a person referencedatabase, which functions to store and model information relating toperson properties. This can include references to bio-identificationmodels. The person reference database can additionally include personalshopping patterns, wishlists, payment information, account settings,and/or any suitable information. Personal shopping patterns can includehistory of past purchases. It can also include personal information suchas allergies, nutritional restrictions or goals, and/or other personalinformation that can inform the EOG system.

Other object reference database components can enable access to otherinformation. For example, an environmental object database can storeinformation related to other present objects such as shelves, bins,carts, baskets, refrigerators, forklifts, and/or other commonlyobservable objects within the environment.

System Integration Options

The system is preferably used in combination with one or more additionalsystems that operates in part based on the EOG. The additional systemspreferably function to inspect object state through the EOG and then totrigger some actions based on that object state. Those actions can beused in altering the state of the system in monitoring the surveillanceof the environment and/or to perform some outside action. Potentialsupplemental system components can include a virtual cart managementsystem 310, a checkout processing system 320, inventory managementsystem 330, a calibration tool 340, a notification system 350, ameta-media storage system 360, a manual task management system 370, amanagement system 380, and/or other integrated system components.

Virtual Cart Management System

In some variations, the system can include a virtual cart managementsystem 310, which functions to facilitate managing commerce model ofcollected items that may be involved in an eventual transaction. Thevirtual cart management system preferably operates on the EOG model indetermining an itemized list of SKU objects that can be assigned to a“virtual cart” or checkout list. A virtual cart refers to the set ofitems selected for a transaction (e.g., a purchase transaction, a rentaltransaction, usage transaction and the like). Here, selection of itemsis interpreted as being associated with a classified shopper object,which can be one or more of a human shopper, a cart, a basket, a bag, abox, and/or more generally a collection of objects in a checkout region.For example, a virtual cart can consist of the objects possessed by ashopper but could alternatively be items contained in a cart, basket,and/or any suitable entity or combination of entities. A virtual cart ispreferably generated in part from the EOG system 200 by detecting theobjects modeled as “possessed” or “contained” by an shopper object. Thevirtual cart management system 310 can manage multiple virtual cartssimultaneously.

The virtual cart management system 310 can include a cart resolutionprocess that inspects the EOG for relevant object state information andtransforms objects from an EOG model to a list of predicted SKU objects.For example, the virtual cart management system 310 can includeconfiguration to identify the set of predicted objects possessed by ashopper and/or contained in a cart when the shopper is in a checkoutregion of a store, and then to convert the set of predicted objects intoa checkout list. That conversion can take into account the confidence ofobject modeling (e.g., classification) and the operational cost ofincluding or not including a product (e.g., impact on customersatisfaction, financial cost of missing a potential sale, etc.).Additionally, supplemental data input can be used in converting from aCV-driven model to a checkout list that reflects operational trends.Heuristical methods, deep learning, and/or other techniques can beapplied during the translation process. In one example, SKU objects areadded to a checkout list based on the classification confidence levels,and confidence level thresholds can be varied based on SKU object price,popularity, and/or other operational data inputs. In some cases, theresolution process may be biased to avoid overcharging a customer or notcharging a customer for a product when confidence in the EOG modeling ofthat object does not satisfy a particular condition. This can functionto layer business operational objectives on top of computationaldetection processes.

The compound object modeling aspect of the EOG can be used so thatobservations in one region can translate into modeling a hidden objectbeing present in a second region. As discussed above and furtherdescribed herein, the EOG does not depend on direct observation ofobjects at a particular location in maintaining the presence of thatobject. In this way, all the items in a full shopping cart that areobscured from view when in a checkout region can be associated with avirtual cart during checkout process.

Checkout Processing System

In some variations, the system can additionally include a checkoutprocessing system 320, which functions to integrate a checkoutprocessing system with the operation of the EOG system 200. The checkoutprocessing system 320 can be used in combination or independently of thevirtual cart management system 310. A checkout processing system 320 canbe a physical system like a point of sale (POS) kiosk that facilitatesthe checkout process. The checkout processing system 320 can be anintegration used within the system to access data (e.g., barcode readerdata, transaction data, etc.) from the checkout processing systemdirectly or indirectly and/or by update state of a checkout processingsystem such as by prepopulating a POS device with the checkout list orproduct prediction data input. Alternatively, the checkout processingsystem 320 can be a checkout processing system 320 that includes apayment input, user interface components (e.g., screen, input device,etc.), and/or other basic components. In one variation, the checkoutprocessing system 320 can include an image capture device 110 that is ina POS capture configuration. A POS capture configuration can be a cameradirected at an item scanning region of the checkout processing system320 such as a conveyor belt, barcode scanning region, an item scale,and/or any suitable region of a physical checkout processing system. Inanother variation, the checkout processing system 320 may be anapplication operable on a computing device such as a smart phone, atablet, a wearable computer, or any suitable computing device. Anapplication variation of the checkout processing system 320 can includecamera integration with the computing device and may include a paymentinput device such as a payment card reader or other suitable paymentinput devices. In another variation, a customer-facing application cancommunicate a checkout list, which can be reviewed and optionallyconfirmed by a customer when completing a checkout process.

The checkout processing system 320 preferably can be used as: a trainingcomponent of the EOG system 200 and/or a controlled output of the EOGsystem 200.

In a training operating mode, the checkout processing system 320preferably functions to provide additional training data of productimages. For example, an image capture device no in a POS captureconfiguration can collect product images while a barcode scanner and/orother POS inputs is used to collect product identification information.A corpus of training data can be collected and maintained through normalproduct checkout processing. The training operating mode of the checkoutprocessing system 320 can be used in onboarding a new store with aunique set of products by generating a set of labeled image data forproducts. Similarly, new products or changes in packaging can beautomatically enrolled because of the training operating mode of thecheckout processing system 320.

Additionally or alternatively, the training operating mode may be usedin combination with active use of the EOG system 200 and the virtualcart management system 310. A modeled virtual cart can be generated fora shopper object. If that shopper object then uses a traditionalcheckout process then the results of the traditional checkout processcan be compared to the virtual cart. Discrepancies can be used inupdating and training the system. In addition to or alternatively,updated information confirmed during a traditional checkout process canbe propagated through the EOG model so as to augment modeling.

An input operating mode of the checkout processing system 320 preferablyfunctions to enable the EOG system 200 to augment and/or control acheckout processing system 320. In one variation, a checkout processingsystem 320 used by a worker or a customer to pay could have itemsautomatically added to expedite the checkout process. For example, acustomer could approach the checkout processing system 320, and withoutactively scanning an item, the checkout processing system 320 could beupdated when the shopper is detected in the vicinity of the checkoutprocessing system 320 to include the products in the virtual cart asindicated by the virtual cart management system 310. The itemsautomatically loaded for checkout can be a full list of items or apartial list. For example, products with a prediction confidence levelabove a threshold can be automatically added, and products with aprediction confidence level below a threshold may need confirmationand/or depend on manual entry. In another variation, the input operatingmode may bias the checkout processing system 320 so that input of anitem can be enhanced. For example, the EOG may be able to detect thatsome quantity of a variety of apples are probably selected by theshopper; a display on a checkout kiosk could display the apple option ona top-level menu so that the worker or customer can quickly select thatproduct instead of navigating a menu of options to select the product.

Inventory Management System

In some variations, the system can include an inventory managementsystem 330, which functions as an EOG interface for inspecting andobserving inventory state. The inventory management system 330preferably includes a user accessible user interface for accessinginventory information. The inventory management system can includeconfiguration to prepare reports related to inventory totals, inventoryordering information, inventory locations, inventory shelf-life,inventory shopper interactions, and/or any suitable information. Theinventory management system 330 can additionally include an inventoryordering system such that the system can be used to at least partiallyautomate inventory ordering.

The inventory management system 330 can additionally include aninterface for augmenting inventory state information, which can be usedto apply user input in updating the EOG model. For example, the EOGmodel may detect that a crate was received and that the crate ispredicted to contain some number of items with some confidence level. Anadministrator could confirm the contents of the crate, alter theidentity of contained items, alter the count of contained items, or makemay any adjustments.

Calibration Tool

A calibration tool 340 functions to enable calibration of objectmodeling and/or resolve model issues. The calibration tool 340preferably communicates to the EOG system 200 to provide updatedcalibration data input concerning objects in the environment. Thecalibration tool 340 preferably provides input used in SKU objectidentification, counting of objects, confirming the absence of anobject, and/or otherwise updating the state of the EOG mode with highconfidence input. The calibration tool 340 is preferably a mobile deviceused by a worker or administrator of the system. In typical usage, aworker can go to various locations in the environment and collect input.Alternatively, a calibration tool could be integrated into aconsumer-facing application as a background service such that throughnormal usage, the application can be used in calibrating the EOG system200.

In one variation, the collection of input can be directed such thatareas with low confidence modeling can be resolved. For example, if ashopper shuffling through items has resulted in low confidence modelingin the EOG, then a worker can be notified and directed to use thecalibration tool 340 in that area to determine the identity and numberof items in that region. This can return that region to high confidencemodeling. In combination with model propagation, changes in modeling inone region can translate to propagating the update to alter the objectstate of an object in another region. The calibration tool canadditionally be used in indirectly enhancing modeling of a shopper whenupdated information from the calibration tool 340 is propagated by theEOG system such that objects possessed by the shopper are moreconfidently modeled.

An image capture device serving as a mobile configuration can beincluded in the calibration tool 340. Alternatively, a barcode scanner,an RFID tag reader, or any suitable product identification input devicescan be used. Alternatively, a user interface input can enable a workerto manually enter information.

The calibration tool 340 can be an application operable on a computingdevice such as a smart phone, a tablet, or a wearable computer device.The calibration tool 340 could alternatively be a custom device. Forexample, the calibration tool 340 could be a computing device with adisplay and user input that also includes a barcode scanner andoptionally an integrated image capture device no. The custom calibrationtool 340 could communicate with the EOG system 200 through a network orby directly communicating with a communication channel of the system.

The calibration tool 340 can additionally include a positioning elementsuch that the location within the environment can be confirmed. Thepositioning element can facilitate detection of environment locationthrough the image data collected by the imaging system 100. For example,a visual beacon or marker could be displayed by the calibration tool340. Alternative positioning technology such as RF beacon triangulationtechnology or other positioning systems may be used.

Notification System

A notification system functions to enable generation of alerts andfeedback. The notification system 350 can include interfaces configuredfor transmission of notifications over various communication channels.This can be used in alerting workers. For example, workers may benotified of a calibration request so that a worker can use thecalibration tool 340 to resolve some issue. In another example, a workermay be notified to assist a customer that is associated with a lowconfidence modeling state. This low confidence modeling state could be aresult of normal customer behavior but could also be through userbehavioral targeted to confuse the system. The notification system 350could additionally include programmatic interfaces such that the variousother systems and services can be updated or integrated in response tovarious conditions in the EOG.

The system could additionally include a rules engine that inspects theEOG model for particular conditions and uses that data in triggeringdifferent actions or events. These actions can be internal actions butcould also be actions with outside systems. For example, a programmaticinterface of the notification system 350 could be used in triggeringsome action at an outside service or communicating updated data to anoutside service.

Meta-Media Storage System

A meta-media storage system 360 functions to store media in a dynamicformat that mixes media data and metadata observations of theenvironment. The meta-media storage system 360 is preferably configuredto store a dynamic media content representation in coordination with theenvironmental object graph. As opposed to traditional surveillancesystems that store captured media in the original or a compressedversion of the media, the system and method can use EOG modeling toalleviate dependence on maintaining a media file on record for eachmoment. Preferably, media is stored for image observations of objectsmodeled with low confidence. Additionally, the media quality mayadditionally be dynamically adjusted. For example, very low confidenceobservations may result in high quality media storage, moderate levelconfidence may result in medium quality media storage, and highconfidence in EOG modeling may result in metadata only storage. Inaddition to media quality, the media representation could be dynamicallyisolated to subregions of image data, where only media related to visualobservations of low confidence modeling may be stored.

The meta-data storage system 360 could additionally include a meta-mediaplayer system that is configured to synthesize the meta-media data intoa representation of the visual observations. Meta-data modeling can beused to procedurally simulate, generate media, or enhance existing mediadata.

Manual Task Management System

A manual task management system 370 functions to augment operation ofthe system with manual operators used in facilitating image processingor analysis. The manual task management system 370 preferably selectsworker tasks, delegates the worker tasks to a worker, and theninterfaces with the EOG system 200 and/or the notification system 350 intaking appropriate action. The manual task management system 370 ispreferably used in scenarios where object classification and/or EOGmodeling have low confidence. Workers can be used to supplement theprocess by inspecting the image data and providing some analysis. In onevariation, a manual task request can be triggered when an object can'tbe confidently classified. In the manual task request, a worker may berequested to select from possible object options. In another variation,the worker may directly enter a classification of the object (e.g., anidentifier of a SKU, a description of the object, etc.). In anothervariation, a manual task request can collect multiple instances of imagedata associated with events involving an object and request analysis ofa chain of observations. This may be used in detecting people attemptingto prevent accurate modeling.

The manual task management system can work in connection with thenotification system and a rule engine such that modeling conditions thatsatisfy a condition can trigger a manual task request to have a personprovide analysis and input.

The manual task management system 370 may redundantly delegate workertasks and/or delegate worker tasks to appropriate workers. For example,product identification may be delegated to a junior worker and a workertask of verifying suspicious shopper behavior can be delegated to amanager.

Management System

The system may additionally include a management system 380 thatfunctions to provide administrators an interface for inspecting and/oraltering operational options within the system. The management system380 can be used in setting operational options and configuring thesystem.

The system could additionally or alternatively include or be integratedwith various other components relating to inventory management,commerce, worker management, consumer-facing services, and the like.

3. Method

As shown in FIG. ii, a method for environmental object modeling of apreferred embodiment can include collecting image data across anenvironment S100, maintaining an EOG from the image data S200, andinspecting the environmental object graph and executing an associatedaction S300. The EOG is preferably a data representation of classifiedobjects in space and time across the environment, and the EOG preferablyincludes at least a subset of object instances having probabilisticobject associations. Maintaining the EOG S200 is preferably an iterativeprocess comprised of multiple process instances comprising: classifyingobjects from the image data S210, tracking object locations in theenvironment S220, detecting interaction events S230, and/or updating theEOG S240. The method is preferably applied across a plurality of objectswithin an environment, and the method is preferably iterativelyperformed to maintain a statistically accurate representation of theobjects within the environment. The method can be implemented incombination with the system above or any suitable alternative system.

The method can apply additional approaches in a variety of ways.Implementations of maintaining the EOG can include EOG associatedprocesses such as compound object modeling, probabilistic modeling ofmultiple object states, and longitudinal object tracking. Additionalprocesses of the method can include scoped object classification andapplying operational biasing.

Compound object modeling in maintaining the EOG can enable detectingobjects as having a classification and a probability of containing otherobjects. Probabilistic modeling of multiple object states can enable themethod to maintain different object states. Longitudinal object trackingcan be facilitated through propagating object state across objectassociations in the EOG—observational data at different times and placescan further refine object modeling in the EOG. Scoped objectclassification can enhance object classification by contextually settingand adjusting the set of candidate objects used in CV-based objectdetection. Operational biasing can enable other data inputs such asother sensed inputs or data sources to alter the execution of an EOG.

The method can propagate certainty through an EOG (e.g., backwards intime and forwards in time) as more information is collected. The use ofthe EOG modeling approaches function to make the method robust inresponse to a limited, intermittent, or temporarily unclear flow ofinput. The method can tolerate and operate on imperfect information,while seeking to improve its knowledge through subsequent observations.The burden of precision can be reduced in some areas, and delayed inmany others. For example, the method can maintain operationally reliablepredictions of products that were selected by a shopper and then held bythe shopper, added to a bag, or added to a cart as the shopper wasmoving through the store. This can then be used in automaticallycompleting a checkout process wherein predicted products are credited toa shopper account and/or by automatically populating a checkoutprocessing system with the predicted products. As another example,inventory tracking can be maintained with usable accuracy even whenobjects are obscured on a shelf or in a storage container. The method ispreferably implemented through a system such as the one described above,but any suitable type of system may alternatively be used.

In one implementation, the method can function to facilitate a shoppingcheckout process. In one variation, facilitating a shopping checkoutprocess can include assisting an existing checkout process. For example,self-checkout kiosks could be made more efficient by using probabilisticunderstanding of shopper possessed items to make scanning of itemsfaster. In another variation, the method may be used for automaticallychecking out a shopper. For example, the method may be used to detectthe objects selected by a shopper, identify a shopping account of theshopper, and charge the shopping account for the selected items uponleaving the store.

The method may additionally function to facilitate identification ofunknown objects at checkout time. When a previously unknown object isobserved to have had its barcode scanned, the resulting SKU canaffirmatively be associated with that object, and future objects havingsimilar appearance and/or properties can be assigned a higher likelihoodof bearing that same barcode and SKU. After many such scanning events, amuch higher likelihood can be assigned to subsequent objects withsimilar appearance having the same SKU number. In this way, machinelearning deep-learning, statistical modeling, and/or other machineintelligence strategies can be employed to continually improve on areasof weakness. In some cases, inventory items could be automaticallyenrolled into the system through such a process such that manualon-boarding of new products may be avoided.

The method can track inventory within an environment by building andevolving a logical representation of the environment and its contents.Generally, the tracking of inventory can involve counting or estimatinginventory items. The tracking of inventory may additionally includetracking the location of inventory items. The tracking may additionallyinclude monitoring object groupings or object-to-object associations.For example, the system may be able to track that one object was carriedby a shopper before being set down on a shelf. Object relationshiptracking can have particular applications for objects that haverestrictions on how they are stored or managed. For example, ice creamthat was removed from the freezer for a particular duration may bedetected, and a worker can be notified to move the item back into thefreezer.

The method may additionally function to supplement security surveillanceor other visual surveillance applications. In one variation, the methodcan trigger an alert when a person attempts to leave the store with anunpaid item. In another variation, the method can be used in convertingsurveillance data to a data representation, which would be moreefficient for long-term data storage. The method can additionally beused in triaging in order to trigger the long-term archiving ofsurveillance media associated with interactions that may benefit frommedia recording.

The method can additionally or alternatively be used in otherapplications. In one variation, the method may be used in inventorylocating. Workers or shoppers may be able to search for the location ofan item. In another variation, the method may be used in inventorystocking automation. The method may be used in reordering products ormonitoring the shelf life of perishable items. The method couldalternatively be a generalized platform with a programmatic interfacesuch that custom applications and system/service integrations canoperate in coordination with the method.

Collecting Image Data

Block S100, which includes collecting image data across an environment,functions to collect video, pictures, or other imagery of a regioncontaining objects of interest (e.g., inventory items). Preferably,collecting image data occurs from a variety of capture points whereincollecting image data includes collecting image data from multiple imagecapture devices (e.g., cameras) distributed at distinct points in theenvironment. The set of capture points can include overlapping and/ornon-overlapping views of monitored regions in an environment. The set ofcapture points can additionally establish a high density imaging systemwithin the environment. Alternatively, the method may utilize a singleimaging device. The image data preferably substantially covers acontinuous region. However, the method can accommodate for holes, gaps,uninspected regions, and/or noncontiguous regions. In particular, themethod may be robust for handling areas inappropriate for image-basedsurveillance such as bathrooms and changing rooms. In these cases,statistical predictions during EOG execution can account forunobservable events.

As described above, the method may not be dependent on precise, exactinformation, and thus the collection of image data may be from anexisting video surveillance system. The image data may be directlycollected, and may be communicated to an appropriate processing system.The image data may be of a single format, but the image data mayalternatively include a set of different image data formats. The imagedata can include high resolution video, low resolution video,photographs from distinct points in time, image data from a fixed pointof view, image data from an actuating camera, visual spectrum imagedata, infrared image data, 3D depth sensing image data, parallax, lidar,radar, sonar, passive illumination, active illumination, and/or anysuitable type of image data.

While, the method may be used with a variety of imaging systems, andcollecting image data may additionally include collecting image datafrom a set of imaging devices wherein each subset is collected imagingdevices in at least one of a set of configurations. The imaging deviceconfigurations can include: an inventory storage capture configuration,an interaction capture configuration, an object identification captureconfiguration, a movable configuration, a blended configuration, and/orany suitable other type of configuration. While an imaging device may beprimarily set for one or more configurations, an imaging device may beused for any suitable purpose.

Collecting data from an imaging device in an inventory storage captureconfiguration functions to collect image data directed at a nominalinventory storage location. For example, a dedicated camera in aninventory storage configuration may be directed at storage shelves. Aninventory storage configuration may be particularly suited formonitoring changes in stored objects.

Collecting data from an imaging device in an interaction captureconfiguration functions to detect the nature of an interaction betweenobjects such as interactions between a shopper and a shelf or container.In one variation, an imaging device can be directed along a plane ofinteraction. For example, a camera may capture video down an aisle so asto detect when a shopper object has a physical interaction with items ona shelf. In some cases, multiple imaging devices may be used incombination. For example, a imaging device directed down an aisle maydetect when a shopper crosses a defined shelf interaction plane, whileimaging device in an inventory storage capture configuration can be usedto determine the location along the aisle. An interaction captureconfiguration may be particularly configured for detecting occurrencesof one or more types of interaction events.

Collecting data from an imaging device in an object identificationcapture configuration functions to target object identification inparticular locations. An object identification capture configuration mayinclude a higher resolution camera directed so as to identify productswhen in a particular inspection region. A limited number of objectidentification capture configurations can resolve uncertainty in theEOG. In particular, they may be used to resolve shopper objects. In astore where shopping carts are used, an object identification captureconfiguration may include a camera directed down at carts that passbeneath. Objects inside the cart may be more easily identified asspecific product items. Such identification configurations may belocated at key locations such as high traffic areas, near store exits,ends of aisles, or other suitable locations. In one variation, an objectidentification imaging device may be actuated so as to track or targetparticular objects. Object identification capture configurations mayadditionally or alternatively be used in a checkout or POS configurationvariation. A checkout variation functions to collect object informationfor identification during a checkout process. A camera in a checkoutconfiguration may be particularly configured to capture items as theyare put on a conveyor belt or set out for inspection by a worker.

Collecting data from an imaging device in a movable captureconfiguration functions to generate image data from a variety oflocations, possibly including dynamic focus. The movable imaging devicemay be mounted to a robot or moved by a worker. Note that in this case,a moveable robot may be one that has the ability to translatehorizontally, or vertically, within the environment, or merely has theability to pan, tilt, or zoom from a fixed location. An imaging devicewith a movable configuration capable of movement through the environmentoften utilizes the tracking location of the imaging device. In onevariation, a movable imaging device can include an active positioningsystem that uses communications between the imaging device and remoteelements used to determine location such as precision GPS system, RFtriangulation or other suitable techniques. In another variation, themethod can include tracking the movable imaging device through theenvironment through image data. A visible beacon may be present on theimaging device to facilitate identification and tracking of the movableimaging device.

An imaging device in a movable configuration may be used in resolvingand possibly reducing uncertainty. For example, if the EOG of inventoryfor one particular area of a store is unknown, or has a low level ofcertainty, then a worker may take a movable imaging device (e.g., acalibration tool) to collect additional image data for the purpose ofresolving the uncertainty. More generally, workers could be equippedwith movable image devices so that as they move about the environment,image data can be collected that may improve accuracy. The EOG canpreferably increase the certainty of the inventory representations withtargeted image capture through use of one or more movable imagingdevices.

It should be noted that any individual camera or sensor may be a part ofmore than one configuration at a time and each configuration may servemore than one purpose.

Collecting image data may additionally include calibrating an imagingsystem. In one variation, calibration can include detecting a signalingpoint in the image data and associating the location of the signalingpoint in the real world to its location in the EOG representation, as itis built up from one or more source of images. The signaling point couldbe a light emitted from a location device such as a bulb, led, or smartphone; or a feature or features of the natural scene.

Maintaining the EOG

Block S200, which includes maintaining the EOG from the image data,functions to generate, execute, and update a probabilistic data modelfor objects in an environment. As discussed, the EOG is a datarepresentation of classified objects in space and time across theenvironment. Additionally, the modeled object instances in an EOG canhave object classifications, location, timestamps, and/or otherproperties such as SKU or product associated properties. The EOG canadditionally be characterized as having at least a subset of objectinstances that have probabilistic object associations, where thoseassociations can be between different object instances in time andspace.

Managing the EOG can include iteratively processing the image data andapplying various EOG processes. The EOG can use its adaptive accountingapproach to leverage various image-based detection techniques. Variouscomputer vision and signal processing approaches can be used ingenerating metadata from the image data. The image data can then beprocessed within an EOG processing engine to update an EOG modelrepresentation of the physical world. Maintaining the EOG can be used inbuilding a probabilistic account of inventory items and other objects inthe environment. Some objects may be affirmatively identified whileother objects may be accounted for with varying levels of certainty.

In addition to use of image data, maintaining the EOG may utilize otherobject and environmental data inputs such as inbound/outbound productorders, itemized POS checkout reports, manual inventory inputs, productreference databases, environment planograms or maps, personal shoppingpatterns, personal shopping lists or wishlists, demographic pattern,and/or other object inputs as shown in FIG. 12.

Managing an EOG system preferably includes iteratively: classifyingobjects from the image data S210, tracking object locations in theenvironment S220, detecting interaction events S230, and/or updating theEOG S240 which preferably comprises propagating a change in an objectinstance across associations of the object instance.

Blocks S210, S220, and S230 can be used in combination in series or inparallel. In one variation, block S200 can execute primarily based oninteraction events wherein blocks S210, S230, and S240 are employed. Inanother variation, blocks classification and tracking can be usedwherein blocks S210, S220, and S240 are employed.

Processing the input can establish base object modeling which mayutilize compound object modeling, probabilistic multi-state modeling,along with regular object modeling as shown in FIG. 13. Block S240 canthen leverage the base object modeling in refining the EOG bypropagating updated object model data through the EOG. The compoundobject modeling can be used where object transformations can be modeledwhen some interaction event alters object state. In other instances orimplementations, object classification of block S210 and trackingobjects in S220 may be used in establishing various observed objectpaths that can be associated through probabilistic reasoning andpropagation of information through the model in block S240.

Through block S200 an EOG can be used to report on expectations ofobject state for a multiple of objects in an environment. As such S200can involve multiple parallel processes modeling multiple objectsexpected in the environment based on image data and/or supplementalinput. The processes can be executed iteratively for each stream ofimage data from a camera but then additionally executed across imagedata from collections of image capture devices. The EOG is generallyreal-time and location aware, which can enable the method to drive avariety of applications such as managing automated self-checkout,assisting POS checkout, providing inventory analytics, providing theftdetection, enabling product locating, and other suitable applications.

In some variations, the method can include executing multiple instancesof block S200 wherein the method includes managing multiple EOGs as inthe exemplary mall scenario shown in FIG. 3, wherein a first EOG ismanaged for a first subregion of the environment and a second EOG ismanaged for a second subregion of the environment. The subregions can benon-contiguous or overlapping. In one variation, the first subregion canfully contain the second subregion. Any suitable number of EOGs fordistinct subregions can be established. Managing multiple EOGs can beused in multi-vendor environments such as a mall or a marketplace. Thedifferent EOGs may operate using image data from a shared imagingsystem, but the configuration and application of the EOGs can beindividually customized. Alternatively, a single EOG can model multipleregions and subregions wherein inspection of that EOG can use spatialand temporal boundaries and simulating partitioned and distinct EOGs fordifferent subregions.

Maintaining EOG: Classifying Objects

Block S210, which includes classifying objects from the image data,functions to perform object detection. Objects are detected andclassified using computer vision or other forms of programmaticheuristics, artificial intelligence, machine learning, statisticalmodeling, and/or other suitable approaches. Object classification caninclude image segmentation and object identification as part of objectclassification. Resulting output of classifying objects of image data ofa single image or video stream can be a label or probabilisticdistribution of potential labels of objects, and a region/locationproperty of that object. Classifying objects in a single image of theimage data can yield multiple object classifications in various regions.For example, an image of a shelf of products with a shopper present canyield classifications for each visible product, the shelf, and theshopper as shown in FIG. 14. Also shown in FIG. 14, additional featuresof the method can additionally result in the EOG modeling expectednumber of items obscured from view.

Various techniques may be employed in object classification such as a“bag of features” approach, convolutional neural networks (CNN),statistical machine learning, or other suitable approaches. Neuralnetworks or CNNS such as Fast regional-CNN (r-CNN), Faster R-CNN, MaskR-CNN, and/or other neural network variations and implementations can beexecuted as computer vision driven object classification processes.

Image feature extraction and classification is an additional oralternative approach, which may use processes like visual words,constellation of feature classification, and bag-of-words classificationprocesses. These and other classification techniques can include use ofscale-invariant feature transform (SIFT), speeded up robust features(SURF), various feature extraction techniques, cascade classifiers,Naive-Bayes, support vector machines, and/or other suitable techniques.

Additionally, multiple variations of algorithmic approaches can beimplemented in accounting for particular classes of objectclassification. A hierarchical classification process can be used initeratively refining classification and/or bounding the classificationchallenge for enhancing classification confidence and/or speed. In onevariation, classifying objects can be limited or isolated to updatingbased on changes in image data. In one variation, classifying objects ofimage can be limited to subregions of the image data satisfying a changecondition. For example, an image of a shelf of products with a shopperin the lower right quadrant of the image may only have objectclassification executed for a region within that lower right quadrant,which can alleviate the method from reclassifying products that arestatic in the image data.

In some variations, object classification can be actively confirmed orinformed through another data input channel. For example, a calibrationtool may be used for logging an object with a confirmed classification(e.g., a SKU identifier), location, and time.

Classifying an object can include identifying an object as one of a setof possible object types, wherein possible object types comprise atleast a SKU object type, a compound object type, and a person objecttype. An object classification can be a descriptor with various levelsof specificity ranging from an unknown object classification to ageneral descriptor to a specific item name. Different classificationsmay be associated with additional classification properties such as SKUproperties for SKU objects, user accounts for customer objects, and/orworker profiles for worker objects. For example, one item could beclassified as a box but could also be classified as a particular cerealproduct with an associated product SKU. Generally, the objectclassification process will generate a set of object classificationpossibilities with varying levels of confidence levels.

In a compound object modeling approach, the EOG can model an object ashaving some probability of containing zero, one, or more other objects.A classification may be used to generate an object compositionprediction during compound object modeling. Applying compound objectmodeling preferably includes instantiating a hierarchical associationbetween at least two objects in the EOG where one object instance is setto probabilistically have an association with at least one object. Ifthis is the first classification, compound object modeling can be aninitiation process based on statistical models that can be based on theobject classification, object location, time, and/or other factors orcombinations of factors. Compound object modeling can facilitate objectcount estimations and/or associative changes in an object (e.g.,tracking an unobservable child object through movement of parentobject). In some variations, the object composition prediction can begenerated for new occurrences of an object that has been observed tocommonly contain particular objects. For example, an object classifiedas a box used to ship bars of soap is given a very high likelihood ofcontaining a standard shipment of bars of soap. Alternatively, thecomposition prediction of an object may be dynamically created based onthe context in which the object is detected. In one example, an unknownbox may be detected and classified as an unknown box, but because it wasdetected in a particular region, the object composition can be set withobjects commonly found in boxes in that region. In another example, anunknown box of goods may be detected and initially suspected ofcontaining a set of different objects with a low probability. As moreinformation is collected on the box though, the classification can berefined and updated through block S220, S230, and S240.

The classification of objects can additionally include assigning objectattributes such as size, shape, color, location, and/or other attributesof the object. An object size may include various dimensions such aslength, height, width, dimension, shape, aspect ratio, and/or volume.Shape may be an attribute describing the form of the object. Shape mayinclude aspect ratio or other metrics. A color attribute may be adescription of the surface of the item and may include an average color,color set, a pattern or texture, reflexivity, various surfacetransforms, and other suitable surfaced qualities. Location ispreferably a three-dimensional location description within theenvironment, but may alternatively be a location categorization such as“aisle 3, bin 1, shelf 2”. Location may be relative with respect to theframe of the environment, or absolute in thelatitude-longitude-elevation frame of the earth. The various objectattributes are stored within the EOG and can used by in in makingdeterminations. For example, the volume and shape may place restrictionson the quantity and type of contained objects. Similarly, location maynot explicitly indicate what objects are contained within a detectedobject, but can provide context for expectations and tracking.

In some scenarios, an object may be affirmatively identified as aparticular item. Object identification preferably results in classifyingthe object with a specific SKU. Identification of an object may be madethrough image matching of packaging, scanning a computer readable code(e.g., a barcode or QR code), OCR of labeling, and/or any suitableapproach. The certainty of the identification is recorded and tracked.For example, reading a UPC barcode, with valid checksum, conveys a veryhigh certainty of identification. Conversely, observing that an objectwas taken from a region of shelving that was known to mostly contain 14oz. boxes of Snack-Os conveys a fairly low certainty. In that case,subsequent inspecting is queued, and future images will be specificallyinspected in order to raise the certainty associated with that, nowpast, event.

In other scenarios, objects may be people, animals, or other indivisibleelements. People and animals may be identified through varioustechniques such as facial recognition, gait recognition, clothingrecognition, associated device recognition (detecting a user's phone) orgeneral object identification. Identification of a person couldadditionally be used in identifying a user account associated with theperson identity.

The EOG system can additionally be applied to draw conclusions aboutreal-world items when image data of them is partially or fully obscured.In one scenario, an object in the physical world that is placed in anunviewable area can nevertheless still be tracked by its logicalrepresentation within the EOG. The EOG maintains object permanence, inthe sense understood by Jean Piaget, not accomplished by most existingcomputer vision systems. Furthermore, the modeling of multiple objectstates can enable multiple possible, unobserved object states to bemaintained and tracked during different events such that they may beresolved to be modeled with higher confidence with additionalobservational data.

In another scenario, regions of the environment are treated as objectsin the EOG in that the environmental region “contains” objects like amodeled compound object. Environment entrances, environment exits,unmonitored rooms, unmonitored regions of the environment can be treatedas objects. For example, a bathroom may be a configured object with anaccess point at the bathroom door. Objects that interact with thebathroom door (e.g., enter and exit) may result in changes in thecontents of that bathroom object.

As shown in FIG. 13, classifying an object and more generally managingthe EOG can include retrieving supplementary inputs used in classifyingan object S212. The classification of an object can be at leastpartially based on the supplementary inputs. The supplementary inputscan be selected from the set of purchase patterns, customer purchasehistory, product pricing, inventory data (e.g., shipment orders,inventory maps/planograms, etc.), environmental data patterns, purchasetransaction data, and/or other sources. Classifying can then be at leastpartially based on the supplementary inputs. For example, productpopularity based on store shopping history may be used to prioritizeparticular classifications over other classifications when CV-basedclassification confidence levels are similar. Block S212 can be useddirectly in classification; but could additionally or alternatively beused in resolving conclusions from EOG such as determining a virtualcart list.

As shown in FIG. 12, classifying an object can include classifying anobject through an iteratively expanding classification scope duringclassification processing S214. Iteratively expanding classificationscope can include setting a classification scope, generating aclassification within the classification scope, and attemptingclassification with an updated classification scope if the generatedclassification does not satisfy a classification condition (e.g., if theresult has confidence level below a threshold). In implementation, blockS214 can involve classifying an object through an initial classificationmodel of a first set of candidate objects and classifying the objectthrough at least a second expanded classification model of a second setof candidate objects if classification of the object through the initialclassification model does not satisfy a classification condition.

A classification scope preferably characterizes a limited set ofcandidate objects of a classification process. In one example, thiscould be a CNN trained on a limited set of objects. Multiple CNN modelscould be trained and updated using different classification scopes suchthat they can be selectively employed based on the desiredclassification scope. Classification scope can be determined through avariety of techniques or algorithmically determined through learning.

In one variation, the classification scope can expand based on expectedspatial proximity of particular object classifications to a region. Thismay include expected SKU objects observable in a particular region. Thatspatial proximity can be iteratively expanded with the objective ofeventually determining a classification with a satisfactory confidencelevel. In one example of spatially expanding classification scope, theclassification scope progress as: objects of a shelf, objects onneighboring shelves, objects on aisle, objects in store region, objectsin store.

In another variation, the classification scope can expand based ondata-driven patterns. For example, customer shopping lists and/orwishlists, customer shopping patterns, various demographic shoppingpatterns and storewide shopping patterns can be used in defining aclassification scope. The utilized classification scopes could includedifferent combinations of spatially defined classification scopes,data-driven classification scopes, and/or other suitable classificationscopes. For example, an initial classification scope can include astored customer shopping list and expected shelved products and asubsequent fallback classification scope can include the hundred mostpopular products sold in the store along with products expected to bestored on that aisle.

Additionally, maintaining the EOG can include updating imagingrepresentations of the object. New and evolving packaging, and othervisual changes, can be used to improve object identification over time.Occasionally, a set of functionally identical items, with identicalSKUs, might have different visual appearances. For example, some itemscome in different colors, while sharing a UPC code. Additionally, thepackaging may change periodically. For example, a brand of cerealroutinely changes the visual appearance of its packaging whilemaintaining a common SKU, UPC, and price. Integration with a managementsystem, a checkout processing system, or a product reference databasecan facilitate in classifying evolving object representations.

In association with classifying an object, the method can includestoring a corresponding object instance in the EOG. The object instanceis a data model record associated with the observed or predicted object.The object instance can be stored in a database or any suitable datastorage solution. An object instance can be a data record characterizingobject classification, time of observation, and location of observation.The location of the object instance may be generalized to the imagecapture device from which the classification originated but could morespecifically include coordinates within the image data and/orthree-dimensional coordinates within the environment.

The object instance can establish the base object modeling. Compoundobject modeling and probabilistic multi-state modeling can additionallybe characterized properties of an object instance. Object classificationcan apply probabilistic reasoning in directly modeling compound objector multi-state properties. Object tracking of Block S220 and interactionevent detection of Block S230 can alternatively or additionallycontribute to compound or multi-state modeling.

Additional object properties and/or associations may additionally beadded or established through the data modeling of the system. Forexample, an association with SKU properties can be established whenclassified as a particular SKU object. In another variation, an objectinstance can be associated with media references such that reprocessingof original media can be performed. In another variation, associationwith other object observations can be established. For example, trackingof an object in time may be used to establish an object path that isreflected as an associated sequence of object instances. Higher levelEOG modeling described herein can either be used to update theobservation record. Alternatively, higher level EOG can useobservational records as a raw data layer on which higher level modelingcan be established.

Maintaining EOG: Tracking Objects

Block S220, which includes tracking objects in the environment,functions to monitor the location of an object in establishing an objectpath. Tracking an object can include tracking the object within imagedata from a single image capture device but more preferably tracks theobject across image data from multiple image capture devices. Trackingan object can additionally be used in identifying and associatingobjects across image capture devices.

Tracking an object can include applying CV-based object trackingtechniques like optical flow, algorithmic target locking and targetre-acquisition, data-driven inferences, heuristical processes, and/orother suitable object tracking approaches. CV-based object tracking andalgorithmic locking preferably operate on the image data to determinetranslation of an object. Data-driven inferences may associate objectswith matching or similar data features when in near temporal and spatialproximity “Near” temporal and spatial proximity can be characterized asbeing identified in a similar location around the same time such as twoobjects identified within one to five feet and one second. The temporaland spatial proximity condition could depend on various factors and maybe adjusted for different environments and/or items. Objects in neartemporal and spatial proximity can be objects observed in image datafrom a neighboring instance of image data (e.g., a previous video frameor previously capture image still) or from a window of neighboringinstances. In one variation, a window of neighboring instances can becharacterized by sample count such as the last N media instances (e.g.,last 10 video or still frames). In another variation, a window ofneighboring instances can be characterized by a time window such asmedia instances in the last second.

Tracking objects is preferably accompanied by establishing anassociation of object instances in an object path. In one variation,object instances can be a data model construct that supports pathproperties. Alternatively, object paths can be a data model constructthat can be associated with an object path. Accordingly, trackingobjects preferably includes constructing an object path, where an objectpath establishes a continuous path of an object in time and space. Anobject path can include stationary paths or path segments to account foran object being stationary like a product on a shelf. At some instance,continuous observation of an object may be prevented and an object pathwill terminate. Terminal points of an object path preferably include apoint of appearance and a point of disappearance.

Termination of an object path can invoke compound object modeling and/orprobabilistic modeling of multiple object states to model possibilitiesof the object in association with objects at a termination point of anobject path. Tracking an object can include after detecting aterminating point of an object path of an object, applying compoundobject modeling and/or applying multi-state modeling. In somevariations, detecting interaction events can supplement or replace therole of addressing termination points of an object path.

Applying compound object modeling preferably includes instantiating ahierarchical association between at least two objects in the EOG whereone object instance is set to probabilistically have an association withat least one object. One object is preferably a parent object that isprobabilistically modeled to possess or contain a child object. Applyingcompound object modeling can associate a first object path to a secondobject path if, for example, the first path is concealed by the secondobject path. In this way the first object can be tracked indirectly bytracking the second object. As shown in the exemplary scenario of FIG.15A, an object A may be tracked along an object path, but when thatobject path terminates it can be modeled as being contained by object B.If object A appears in the proximity of object B at the t=20 mark, thenthat object path can be associated with the same object A as at t=1.

Applying multi-state modeling preferably includes instantiating at leasttwo probabilistically possible states of an object instance in the EOG.Applying multi-state modeling can enable multiple possible object pathsto be considered at a termination point. Multi-state modeling can beused in combination with compound modeling where one or more of theprobabilistic states can be a hierarchical association with anotherobject. As shown in the exemplary scenario of FIG. 15B, an object A canbe modeled as probabilistically being contained in one of three objects(B, C, and D) when the object path of object A terminates. As anexample, a product may have a first object state modeled of the objectbeing pushed further into a shelf where it is unobservable or a secondobject state modeled of the product having been picked up and carried bya shopper. Both object states can be modeled until additionalinformation is collected to better inform which of those states wasaccurate. The additional information may be used in confirming one ofthose possible states or to counterfactually eliminate one of thepossible states.

In many scenarios, object paths do not involve uniformly consistentmodeling of an object. The object classification and the associatedconfidence level generated for object instances in block S210 may varyfor object instances associated with an object path. A variation ofblock S220 in combination with block S240 can include propagating objectupdates across an object path, which functions to longitudinally processobject information for a tracked object which can be used in updatingobject classification to supplement or assist object classificationcapabilities of the EOG.

In some instances, this can include weighting classifications withgreater specificity. For example, with an object path where 65% of thetime the object is classified as a general box and 35% of the time theobject is classified as a particular breakfast cereal product,propagation of object classification across the object path will updatethe object to be considered the breakfast cereal product along thatpath. Such classification propagation along object paths can annealobject classification issues. As shown in FIG. 16, an object path mayinitially be modeled as the path of an object classified as a “box”, ata later point an updated object classification may more specificallyclassify the object as ACME cereal with higher confidence. The secondclassification can result in propagating the updated object stateinformation across the object path associations so that earlier objectinstances are modeled as the ACME cereal item.

The method can potentially address a potential challenge ofclassification failing when the object is in motion. However, objecttracking may be maintained during object motion, which can enablepropagation of a confident object classification to periods when theobject is moving. In one example, a breakfast cereal product isconfidently classified as such while it rests on the shelf. When ashopper selects the product and adds it to their shopping bag, the imagedata may capture obscured and/or blurry images of the product, which canresult in low confidence classification. However, object tracking of theobject can enable the high confidence classification while on the shelfto be applied to that tracked object. Then when the object is obstructedfrom viewing while in the bag, compound object modeling can model thatbag as containing the product.

Maintaining EOG: Detecting Objects

Block S230, which includes detecting interaction events, functions toidentify and characterize the nature of changes with at least oneobject. Interaction events can be detectable changes observed in theimage data. Preferably, interaction events can be used in applyingcompound object modeling and multi-state modeling. An interaction eventcan additionally include triggering updating the EOG of block S240 thatcan include propagating new object state information, such thatconclusions from the interaction event can be used longitudinally on theEOG.

The interaction events preferably offer a detection approach with lowcomplexity such that the method does not depend on fallible andspecialized detection algorithms. Preferably detecting interactionevents can include detecting an interaction event through detection ofone or more of a set of interaction event types such as object proximityevent (e.g., detecting object-to-object contact or proximity change)and/or compound object transformations (e.g., detecting objectappearance, object disappearance, object classification change, and thelike). Different variations of such interaction events can additionallybe used depending on involved objects. Processes for specialized andcustom interaction event types such as person-to-object contact, objectinsertion into cart/bag, or other event types can additionally be used.Interaction event conditions can be adjusted depending on differentoperational objectives. In one variation, the sensitivity can bedynamically adjusted. For example, machine learning and/or manualadjustment can be applied in tuning configuration properties of theinteraction events.

An interaction event can be the interaction of one or more objects wherethere is a probabilistic transformation of objects in its environment.An interaction event can be associated with a compound objecttransformation. An interaction event can involve a transfer in either orboth directions between a set of objects, and an interaction event caninvolve the inward or outward accretion of one object by another.Additionally, the transformation may involve identified objects orunidentified objects. As yet another variable, the direction and/oroccurrence of a transformation may be probabilistically characterized.In one example, the exchange of objects could be between two or moreclassified objects such as a shopper object and a shelf-of-items object.In another example, one compound-object may produce another object suchas if an object falls out of a container. In another example, aninteraction event may be detected but the nature of the transformationmay be unknown at the time. Modeling of multiple object states can beemployed to account for different possible outcomes of an interactionevent.

Applying compound object modeling as in classification and trackingpreferably includes instantiating a hierarchical association between atleast two objects in the EOG where one object instance is set toprobabilistically have an association with at least one object. Variousfactors of the interaction event can be used in determining how theinteraction of objects results in changes to hierarchical associations.Additionally, when updating the EOG, previous object instances can beupdated to reflect the compound object modeling that yielded theinteraction event. For example, if an interaction event detects a childobject produced from a parent object, then that parent object instancecan be updated at a previous instance to have contained the childobject.

Applying multi-state modeling as in classification and trackingpreferably includes instantiating at least two probabilisticallypossible states of an object instance in the EOG. Applying multi-statemodeling can enable multiple possible results of an interaction event tobe considered and maintained in the EOG. Multi-state modeling can beused in combination with compound modeling where one or more of theprobabilistic states can be a hierarchical association with anotherobject. For example, when a proximity interaction event is triggered bya shopper and a product, then that product instance can be modeled witha first probability as being in an object state as having been pushedback into the shelf and modeled with a second probability as being in anobject state as being possessed by the shopper. Both object states canbe modeled until additional information is collected to better informwhich of those states was accurate. The additional information may beused in confirming one of those possible states or to counterfactuallyeliminate one of the possible states.

The result of an interaction event may be known wherein the nature ofthe interaction is visually observed. For example, a product may beaffirmatively observed to enter a cart. The EOG can be updated with ahigh degree of certainty that the cart contains the product. In otherscenarios, the understanding of an interaction event may beprobabilistic wherein the nature of the interaction was not fullyobserved or classified. For example, a shopper reaching into a shelf andinteracting with an item may be considered as a transfer of an objectfrom the shelf to the shopper with a first probability, the shoppertransferring an object to the shelf with a second probability and theshopper removing a different object with a third probability. Sometimesthe detection of an interaction event is not coincident with itsunderstanding. In these cases, a subsequent inspection of the shelf andshopper may offer the best information about what objects weretransferred and in which direction. When higher quality information isavailable the EOG can be updated to better represent the originalinteraction event.

An object proximity event results from an increase or change in theproximity of two or more objects. Specifically, detecting an objectproximity event can include detecting proximity of a first object of afirst classification and at least a second object of a secondclassification satisfying a proximity threshold. For example, a personobject coming within a certain distance (e.g., less than an arms widthsuch as two feet) of a SKU object may trigger an object proximity eventbecause there can be the possibility that the person interacted with theobject.

The number, classification, and/or state of objects involved in anobject proximity event may be factors in a condition for detecting anobject proximity event. For example, different proximity thresholds maybe used depending on the object classifications. More specifically,different product objects may have different sensitivity to proximityevents with shoppers, which can be influenced by product popularity,shopper purchase history, product price or other supplemental inputs.

Additionally, an object proximity event may have an associated proximitymagnitude wherein the modeling of the interaction event can be augmentedby the proximity magnitude. The proximity magnitude could be distanceestimation or proximity classification. Time of proximity canadditionally determine or be a factor in the proximity magnitude. Anexample of proximity classifications in decreasing proximity can include“contact”, “near-contact”, and “close proximity”. Preferably, lesssignificance or weighting is applied to modeling with lower proximitymagnitude. For example, object-to-object contact will generally bemodeled with more significance than objects coming within generalproximity.

In alternative variations, proximity conditions can be for discreteinteractions or for continuous interactions. In particular for adiscrete interaction, the proximity of a person object and a storageobject will trigger an interaction event. When a shopper comes incontact or nears a shelf, an interaction event occurs. For example, ashopper that is observed to reach into a shelf could result in aninteraction event that more likely predicts the selection of an object.However, a shopper that is observed to merely be near a shelf may be aninteraction event with a low probability of any exchange of objects. Forcontinuous interaction probability, the likelihood of an interactionevent occurring between two objects can be based on proximity and time.As shown in FIG. 9, the path of a shopper through a store can alter thelikelihood of interactions with all objects within the store, whereinobjects along the path having higher probability of being involved in aninteraction event and objects not along the path being assigned a lowerprobability (or zero probability) of being involved in an interactionevent.

As shown in the exemplary scenario of FIG. 17A, a shopper may approach ashelf. Occlusion of object A may trigger an interaction event thatupdates an object A as possibly being in the shelf or in selected by theshopper. If the shopper walks away, that interaction event incombination with the disappearance of the earlier object A increases theprobability that object A was selected by the shopper. In a contrastingscenario of FIG. 17B, when the shopper walks away the object A may beclassified and the possibility of object A being selected by the shoppercan be reduced or eliminated. As shown in the sequence of exemplaryobservations in FIG. 18, interaction events and modeling are not limitedto two objects.

Detecting an interaction event may additionally be triggered through thedetection in the change of an object. For example, if an inventorystorage configured image capture device detects a change in the layoutof objects on the shelf, then the probability of previous interactionevents is elevated. If a shopper is nearby, even if the shopper wasn'taffirmatively observed to contact the shelf, that shopper may beassigned a higher possibly of having selected an object from that shelf.More specifically a change of an object can involve some form of acompound object transformation wherein detecting an objecttransformation can include detecting an object appearance event, objectdisappearance event, and/or an object classification mutation event.

Variations of object appearance events and object disappearance eventscan include detection of a new object, obscuring or disappearance of anobject, an apparent revealing of an object from a second object,apparent concealing of an object by a second object, an apparentsplitting of an object into multiple objects and the apparent merging ofmultiple objects into a compound object. Additionally, an altered objector mutilated object can be a form of an object appearance event.Affected objects are ones that have been affected by an outside entity.For example, an apple that a customer bites into would undergo an objectinteraction event transforming from an apple to a damaged apple. In onevariation, altered objects can be a type of object classification andcan be addressed within object classification process. Interactionsinvolving the transformation of an object to an altered object may beused in alerting workers, adding the item to a checkout list of theshopper regardless of object selection, or used in any suitable way.

A classification mutation event can involve the classification of anobject changing between different time instances. This classificationmutation may be detected in combination with object tracking todifferentiate between the appearance/disappearance of two distinctobjects and changes in object classification of a single object.

Detecting interaction events may additionally include logginginteraction events such that that the chain of interactions can beinspected in response to updated object instance information. An EOGdata system is preferably updated with historical records or objectrelationships and interactions. In one exemplary scenario, a firstinteraction event is recorded as a shopper selecting product A from ashelf containing product A and B, and later a second interaction eventis recorded as the shopper setting product A back onto a second shelf.If an affirmative identification of a product B is made on the secondshelf, then the EOG may be updated in block S230 through the interactionevent log so that the second interaction event is of a shopper settingproduct B on the second shelf and the first interaction event is updatedto the shopper selecting product B.

As an exemplary scenario, tracking object location (e.g., motion paths)in combination with detecting interaction events can use compound objectmodeling and probabilistic multi-state modeling in generating arepresentation of object state. In FIG. 19A, an object A can be modeledthrough multiple interaction events some of which do not visible includeit. Multi-state modeling can be used in maintaining some representationof possible object states of object A, and when updated classificationof object A eventually occurs, the updated object state can be used toresolve the two possible states into one possible state as shown in FIG.19B.

Maintaining EOG: Updating EOG

Block S240, which includes updating an EOG, functions to modify the datarepresentation of objects and/or the processing of inputs and to betterreflect the state of items in the environment. Updating the EOG caninclude propagating object state of at least one object through the EOG.This can result in updating associated EOG representations, wherein oneor more database records are modified in an EOG data system. Updatingthe EOG may additionally or alternatively include updating an EOGprocessing engine 210, which functions to dynamically modify how data isprocessed and converted into an EOG model.

Generally, updating an EOG results in a chain of updates toprobabilistic understanding of associated objects wherein block S240 caninclude propagating change in at least one object instance across objectassociations in the EOG updated object state through associations in theEOG S242. A change in an object instance is preferably updated objectstate data, which can include changes in classification, location,compound object modeling, multi-state modeling, object creation ordeletion, and/or other updated object information. For example, theaffirmative identification of an object at one instant may alter theprobabilistic understanding of prior interaction events, which may alsoalter the probabilistic understanding of other associated interactionevents. Similarly, supplemental input data can be propagated across theEOG.

Associations of the EOG can include associations to object instancesthat are some way linked through object tracking, interaction events, ordata-driven inferences of object classifications and other patterns.Compound object modeling, multi-state modeling, object paths arepreferably EOG modeling constructs that can characterize theseassociations.

Propagating a change in an object instance can include longitudinallyupdating associated objects with updated object state information, whichfunctions to propagate updates backwards and forwards in time.Propagating updated object state can result in updating classificationof an associated object, updating multi-state modeling, and bridgingobject paths. In one variation, the associations are object instanceassociations with object paths such that object instances associatedwith that path may be updated with new information relating objects ofthat path. In another variation, the associations can be hierarchicalassociations as in compound object modeling. In another variation,associations can be based on spatial and temporal proximityassociations.

In one variation, the updated object state is an updated objectclassification, which can result in updating classification of objectsat other spatial and temporal instances. The classification can be onemade with a different confidence level. More confident classificationscan be used to update object modeling to enhance confidence of otherobject instances (e.g., increasing classification and/or confidence ofan object instance) as shown in FIG. 33. Similarly, multiple instancesof classification can reinforce classification so that confidence can beincreased beyond the confidence levels of a single classificationinstance. Alternative classifications can augment how an object isclassified by either providing a more specific classification or bycalling into question previous classifications. The impact of updatedobject state can additionally be weighted by an image capture devicepriority. For example, an image capture device in a POS captureconfiguration or from a calibration tool may be granted higher priority.A classification could similarly result in decreasing classificationconfidence of another object instance as shown in FIG. 34 and in somecases change the object classification.

In one variation, the updated object state can include altering acompound object association, which can alter a hierarchical associationthat can result in changing a parent-child object association. In somecases, this can remove the hierarchical association for that instance.

Updating multi-state modeling functions to alter multi-state modeling tobetter reflect expectations. Preferably, updated object state data of anobject instance can eventually be propagated to an associated branch ofmultiple potential states, and the updated object state data can be usedto reduce modeling to a single state and/or refine the probabilisticmodeling of the multiple potential states. Refining the probabilisticmodeling of multiple potential states can include changing probabilityand/or changing the set of potential states (reducing, increasing, orotherwise changing the potential object states). For example, aparticular object may be modeled as either possibly being obscured on ashelf or possibly in the cart of a shopper. If the product is observedand classified in the cart at a later time, then the motion path of thecart can be traced back to the point on the shelf and the potentialstate of the object being obscured on the shelf can be eliminated.

Bridging object paths functions to establish associations betweendifferent object paths. There will commonly be gaps or holes in thetracking of an object. Bridging object paths preferably includesidentifying object paths with substantially similar object properties.Bridging object paths can initially query for object paths within a neartemporal and spatial proximity, and then expand that search. Bridgingobject paths can identify object paths from earlier or later instances.Bridging object paths can additionally bridge object paths that haveoverlapping time instances when the object is tracked by multiple imagecapture devices.

Propagation of updated object state can result in cascading modelupdates. As an example, a classification of an object with highconfidence may alter the classification of an object along its entirepath. This change can alter association of that object path with anotherobject path that is predicted to be the same object. This could thenalter the compound object and/or multi-state modeling at a point inassociation with that newly associated object path.

Updating the EOG can be initiated in response to one of the base objectmodeling informed by object classification in block S210, interactionevent detection in block S220, and/or object tracking changes in blockS230. Interaction events will generally include events related to new orupdated classification information of an object, and modeled objectpaths can be processed to bridge or anneal distinct object paths.

In another variation, updating the EOG system is initiated in responseto a change in an object, which may include a change in objectclassification (e.g., object type, object identification, objectproperties, etc.) and/or object relationships (e.g., parent/child objectrelationships. For example, a change in represented size might resultfrom additional visual information. Changes in size may change theavailable volume in which child objects can occupy. In anothervariation, the change in an object is a detect object transformationthat includes revealing contents of an object, concealing an object byanother object, splitting an object into more than one object, merging aset of objects together, appearance of an object, and disappearance ofan object. In this variation, propagating updated object state caninclude: updating a previous object instance to have possessed thatobject, updating a current object instance to have concealed an object,updating a previous object instance to have been a set of mergedobjects, updating a current object instance as a merged object, and thelike.

In another variation, updating the EOG system can additionally includeincorporating supplemental input as a form of a resolution event. Aresolution event generally adds contextual information around theobjects within the environment. Such resolution events can relate toother object inputs with access to inventory metrics or other objectinformation. For example, an external inventory management system thatcoordinates ordering and stocking of inventory may provide context as tothe expected input of additional inventory items in the environment. Inone case, a worker may scan in received shipments before storing in abackroom or in the storefront. In another example, a point of salesystem may provide data on the inventory items expected to be leaving astore. In another example, periodic inventory events may occur whereworkers manually take inventory. Such resolution events can be used inupdating objects in the EOG.

In one variation, the method can additionally include prioritizingobservation of an object, which functions to alter image data collectionand/or EOG processing for a particular object. Objects satisfying aparticular condition may be monitored with particular care. Preferablyobjects are tracked more carefully when there the certainty of itsidentity and/or contents is below a threshold value. In one instance,the collection of image data may be updated to increase the certainty ofthe object's classification. In one instance an imaging device in amovable capture configuration could provide data to identify theproduct. In another instance, a worker could be dispatched to collectdata used in increasing certainty.

The method is preferably performed iteratively such that the EOG isrepeatedly updated and refined. In initiating use of the EOG systemwithin an environment, the method may include managing the EOG systemduring a training mode. When managing the EOG system in a training mode,the method may be performed alongside one or more outside inventoryinput systems such as a checkout processing system or an item orderingsystem. The EOG system can run and calibrate its operation based ontraining input from the outside inventory input systems. When theperformance of the EOG system is satisfactory, the method can transitionto use EOG system in a substantially independent mode, through the useof the outside inventory input may still be used in part for the EOGsystem.

In another variation, maintaining the EOG can additionally include atleast partially resetting the EOG, which can function to reduce EOGmodeling complexity. Resetting the EOG can include discarding lowprobability aspects of the EOG. For example, object state modeling withlow probability multiple-states and/or compound object modeling can beeliminated, reduced, or otherwise simplified. Resetting the EOG canhappen continuously when maintaining the EOG. Alternatively, it couldhappen periodically. For example, the EOG may reset daily betweenbusiness hours.

In one exemplary scenario shown in FIG. 20, a shipping crate may be adetected object with an unknown composition. After an interaction eventof a removed object and affirmative classification of that object as asingle box of cookies, the EOG can be updated with knowledge that theshipping crate had previously been filled with a number of cookie boxes.In the current state, the crate object in the EOG would be updated ashaving been previously filled with cookie boxes and now having one lesscookie box. Estimating a filled state of the shipping crate can use thevolume and dimensions of the shipping crate and cookie box in order toguess at the number of cookie boxes that would fit within the shippingcrate. At that point, the cookie box possessed by a person is classifiedwith a high confidence because of the affirmative classification, andthe shipping crate can be modeled as containing a number of cookie boxeswith a moderate confidence level. The moderate confidence level ispreferably moderate (e.g., not zero and not high level like 10%-60%)because the modeling is a result of soft inferences used in drawingconclusions. If at a later point, a second object is removed andclassified as a different snack box, the EOG would be updated again.This time the shipping crate may be determined to have been originallyfilled with half the box volume for the snack boxes and the other halfdevoted to the cookie boxes, but the confidence level in the contents ofthe shipping crate may be even less. Conversely, if the second objectremoved from the shipping crate was another cookie box identical to thefirst, and the volume calculations matched up, then the probability thatthe reminder of the shipping crate contained the estimated number ofcookie boxes, now less two, would be elevated. When the shipping crateis finally emptied, and the carton discarded, the numbers are checkedagain. If all went as predicted, the EOG system will use that knowledgeto up-weight subsequent predictive probabilities the next time the EOGsystem encounters an object with similar context (e.g., classified as asimilar shipping crate and/or a crate located in a similar location).

In another exemplary scenario shown in FIG. 21, a shelf may includethree objects, where only the front one can be affirmatively classified.In the best of cases, the shelf object would have a history thatindicates the number and type of objects contained. If not, obscuredobjects may be probabilistically presumed to be child objects of theshelf and being the same as the front object. However, if a front objectis removed and a previously obscured object is classified differentlyfrom the original prediction, this may result in reassessing theassumptions about the other obscured objects. This can impact inventoryaccounting as shown in FIG. 21.

Inspecting EOG and Execution

Block S300, which includes inspecting the environmental object graph andexecuting an associated action, functions to utilize and/or interactwith the EOG to affect change. The EOG is preferably inspected toexecute some action. The action can vary depending on the use-case.Inspecting can include querying, accessing, or otherwise processingstate of one or more object instances and associated properties.

Inspection can additionally be automatic inspection triggered by variousrules, conditions, or other algorithms. Object state informationrelating to one or more objects can be identified during inspection. Insome cases, the processing of the object state can be used to resolvethe EOG model into a more workable format. For example, inspection ofthe EOG in a shopping application can result in resolving object staterelating to a collection of objects possessed by a shopper object to alist of products and their prices.

In some variations, the executed action can be used in some applicationsuch as for facilitating a checkout process of a shopper or in directingor managing inventory and operations of a facility. In other variations,the executed action can be related to actions related to maintaining andrefining the EOG such triggering calibration tasks, worker requests,and/or media storage which can have benefits for enhancing thecomputational capabilities when modeling objects in an environment.

EOG Facilitated Checkout

In one particular embodiment, the method may be applied to at leastpartially automated checkout. A checkout embodiment preferably includesestablishing a virtual cart list (i.e., a checkout list) throughassociating classified SKU objects with a shopper object. The shopperobject can be a customer/person object, a cart object, a basketclassified object, a bag classified object and/or some collection ofshopper associated objects (e.g., a shopper object, various bag objects,a basket object and/or a cart object).

Products will generally be added by a shopper to a cart at variouspoints in time and space while moving through a store. Those productsmay not be visible when approaching a checkout area. However, the methodas described can leverage compound object modeling to generate a virtualcart list for checkout when a tracked shopper approaches by identifyingproducts modeled to be contained by that shopper/cart. As oneparticularly unique characteristic, the generation of a virtual cart canbe accomplished by image data spatially and temporally removed from thecheckout process. Actions based on the virtual cart list can be appliedat a time and space distinct from observations leading to the virtualcart list construction as shown in FIG. 35. Alternatively, the methodmay support micro-transactions wherein each item is individually chargedand/or refunded depending on changes in object associations with ashopper object and the generation of a virtual cart list.

In taking action with the virtual cart list, the method can includedetecting the shopper associated object in a checkout region and eithercharging an account of the shopper as shown in FIG. 22 or augmenting acheckout processing system with the virtual cart list as shown in FIG.23. The checkout process can be a product purchase event, but couldsimilarly be accounting for possession of the items in a user account.For example, the automatic checkout embodiment could similarly beapplied in a library or item renting/borrowing applications.

When used in assisting the checkout process, the expected SKU objects inthe virtual cart of the shopper can be communicated to a checkoutprocessing system (e.g., a checkout kiosk).

In a preferred implementation this can include during inspection ofobject state generating a checkout list of SKU/product object instancesassociated with a shopper associated object instance, and when executingan action: communicating the checkout list to a checkout processingsystem in proximity to the shopper associated object instance. Themethod can additionally include: at the checkout processing systempopulating an itemized list of products for a pending transaction of thecheckout processing system and/or biasing selection of product input bythe checkout processing system.

Preferably, the itemized list of products to be purchased isautomatically entered into the checkout processing system based on thelist of SKU objects. In one variation, objects predicted with a highconfidence threshold may be automatically added, and objects within amoderate threshold of confidence can be quickly confirmed throughautomated additional inspection or some user interface action (asopposed to manually scanning in the item). In some variations, objectswith low confidence may be handled in various approaches such asalerting a worker to check for the physical items or requiring theworker to manually scan unadded items. The method may be used tofacilitate checkout with a checkout processing system such as aself-checkout POS system, a consumer-focused application for a mobilecomputing device (e.g., smart phone or wearable computer), and/or astaffed POS system.

The method can alternatively be applied to a fully automatedself-checkout process, wherein a shopper is enabled to select a productand leave the store, alleviating the shopper from using a traditionalcheckout process or kiosk. When applied to automatic checkout, themethod similarly associates SKU objects with a shopper but additionallyincludes associating the shopper with an account and charging theaccount for possessed SKU objects when leaving the store. Associatingthe shopper with an account may use biometric detection techniques suchas identifying the shopper through facial recognition.

In a preferred implementation this can include during inspection ofobject state generating a checkout list of SKU/product object instancesassociated with a shopper associated object instance, and when executingan action: accessing an account associated with the shopper associatedobject instance and charging the checkout list to the user account whenthe shopper associated object instance is detected in a checkout region.

Associating the shopper with an account may alternatively includedetecting a device of the account such as the presence of a connecteddevice (e.g., smart phone or RFID fob). Associating the shopper with anaccount may alternatively include using a credit, debit, gift, orpayment card at a kiosk or checkout station. If no problems are detectedin the shopper object, the shopper may leave the store without anyfurther interaction and a receipt may be conveyed to themelectronically, or printed on the spot. In yet another variation,associating the shopper with an account can involve active registration.For example, a shopper may be requested to “check-in”, “check-out”, orperform some action that enables an account to be mapped to theirpresence and activities within the environment. Bump technology, NFC,QR, and many other channels may be used to accomplish this association.From the shopper's perspective they can walk into the store pick up oneor more products and walk out, and they may receive a notificationthrough an application or messaging medium that they've been charged fora set of items.

An automatic checkout embodiment can additionally include providing acurrent virtual cart list prior to a checkout processing system as shownin FIG. 24, which functions to present a preview of products currentlymodeled as being possessed by a customer. The current virtual cart listcan be provided through a user application communicating with the EOGsystem. A shopper could additionally manually edit the virtual cart listif there are errors. Additionally, a user interface can be presentedthrough which user input can be received thereby used in editing thevirtual cart list and then updating the EOG through S240.

In one variation, a virtual cart is not generated during shopping, andinstead a virtual cart is generated within a checkout region bycollecting SKU objects within the checkout region. This variation couldsupport multiple customers picking up items entering a checkout regionand the combined collection of items is used in generating a virtualcart that can be used in a single purchase transaction. Similarly, thisapproach could flexibly handle the scenario of a shopper that is using acombination of bags, carts, and/or personally holding items on theirperson. From a customer perspective, products that are brought into acheckout region will be automatically added to the virtual cart during acheckout process.

In one variation, prior to acting on a virtual cart, the method caninclude resolving objects associated with a shopper object into avirtual cart list as shown in the exemplary FIG. 25, which functions totransform object modeling to a reduced set of products eligible forpurchase. Primarily only objects eligible for purchase are added to avirtual cart list. This can include selecting SKU objects associatedwith the shopper object. It may additionally include selecting SKUobjects acquired by a shopper object within a shopping region of theenvironment. This can function to account for objects introduced intothe environment by the customer. For example, if a customer brings in acan of soda that is also sold in the store, that can of soda isassociated with the customer during entry into the environment (oralternative by being produced from a purse, bag, etc.) and so would notbe added to a virtual cart list.

Resolving objects associated with the shopper object can additionallyinclude accounting for object classification confidence levels, productpricing, product margins, and/or other operational properties. In manybusiness operations, user experience has its own value, and adjustingthe virtual cart to but also consider customer satisfaction if an erroris made or if they are prevented from using automatic self-checkout ifthe EOG has a slightly less than satisfactory confidence level in theproducts selected for checkout by the shopper.

Checkout System Integration

As described above, the method can leverage integration with a checkoutprocessing system to augment POS operation by supplying a generatedvirtual cart list. This variation as described above generally involvescommunicating a virtual cart list to a checkout processing system. Thevirtual cart list received at the checkout processing system may then beused in prepopulating an item list in the checkout processing system.The checkout processing system can be a worker stationed POS kiosk, acustomer self-checkout kiosk, a customer facing application (operable ona computing device of the customer), and/or any suitable device used tofacilitate the checkout process.

The virtual cart list may alternatively be used in biasing productdetection by the checkout processing system. The checkout processingsystem may use an image capture device and object detection wherein aprepopulated or biased item list can reduce object detection challenges.The checkout processing system may alternatively use a user interface,barcode scanner, and/or any suitable input for adding items forcheckout. These inputs can be biased to more readily detect predicteditems. For example, a user interface may present possible items, and aworker can quickly select them to add them to the cart.

POS integration may additionally be applied in updating EOG modelingand/or training. In one variation, object classification can be trainedby collecting image data at a checkout processing system and applying aproduct label generated by a POS input (e.g., a barcode scanner) assupplemental input in updating the EOG. The collected image data can beused in generating object instances and associations within the EOG thatrelate objects at checkout processing system to other object instancesin the store. The product labels can serve as high confidence objectclassification inputs that can then be propagated across the EOG toother associated object instances. In another variation, a generatedvirtual cart for a shopper using a traditional POS checkout process canbe used in training the EOG system and/or in correcting the objectmodeling related to the shopper and propagating updated object stateinformation through the EOG. For example, a virtual cart list generatedfor a shopper can be compared against an itemized list of productsbilled in a transaction at a checkout processing system. The accuracy ofthe EOG modeling can be trained and errors or updated data can be usedto back propagate through the EOG. This can be done transparently to theshopper.

EOG Facilitated Inventory Management

In an embodiment that is directed to inventory management, the methodcan additionally include providing an inventory management system atleast partially driven through the EOG. The inventory management systemcan include integration with other operational logistics systems such asshipment/order tracking systems and checkout processing systems.However, with integration with the EOG, a provided inventory managementsystem can present granular insights into object tracking through theenvironment.

In one variation providing an inventory management system can includereporting inventory state such as inventory count and/or inventorylocation. An EOG model can be analyzed to generate inventory countestimates. In some cases, the reported inventory count can additionallyreport a range of inventory count variability based on unknowns.Individual inventory item location can additionally be reported and maybe used in generating an inventory location map. That inventory locationmap can be a real-time representation of inventory items within theenvironment. Additionally, object associations modeled in the EOG cansimilarly be exposed in an inventory report. The contents of differentobjects or containers and/or optionally the capacity for additionalobjects can be reported. These objects could be containers or storageelements. This could be used so that an environment could understandinventory capacity capabilities. For example, providing inventorymanagement system can include analyzing contained objects of storageobjects in the EOG and reporting expected storage capacity.

In one implementation, the provided inventory management system canexpose an inventory query service that can be used in locating objectsor determining if the object is in stock. Workers and/or shoppers couldquery a service for a particular item, which uses the EOG system todetermine an approximation of the quantity of the item and expectedlocations of the item. A map may be displayed showing where the item islocated. Alternatively, an augmented reality interface may be used inindicating the location of the item.

Since individual objects can be tracked through the EOG with somegranularity, individual metrics can be monitored across all inventoryitems. For example, providing the inventory management system caninclude tracking activity of objects within the environment. The historyof an object can include different locations where it has been stored,different objects it's been in contact with, the workers or machinesthat have handled it, and/or other activities relating the objecthistory in the environment. In one particular application, trackingactivity of objects within the environment can be applied to reportingstorage/shelf time of inventory items. For perishable goods, the methodmay include associating an object with an expiration data and trackingan expiration profile of inventory. Alerts or notifications can betriggered when particular inventory items near expiration or whenparticular inventory items pass the expiration data and need to bediscarded.

In another variation providing an inventory management system can beused in triggering inventory orders. At a basic level this can includetriggering an order when inventory in the environment drops below somethreshold. In some cases, triggering an inventory order can be automatedbased on internal environmental activity. For example, purchase historycombined with tracking individual item expiration can provide anenhanced understanding of projected inventory state.

In another variation, the inventory management system can be applied toidentify items that are out of their range of normal locations and mightbe lost or misplaced. As an exemplary scenario, the method can find anunbought box of cookies in the soap aisle and prevent reordering moresoap when a large shipping box full of soap is hidden behind a largershipping box of something else.

A provided inventory management system can additionally be used forother alternative applications such as in directing workers, identifyingmisplaced items, accessing a timeline of media streams for a particularobject (which may have applications when item contamination is detectedin a processing environment).

EOG Calibration

The method can additionally include triggering an EOG calibrationrequest, which functions to generate and communicate an alert used inaddressing an issue noticed through the EOG system as shown in FIG. 26.The EOG calibration event can preferably prompt an action to resolveissues with low confidence object modeling. In one implementation, acalibration process of the EOG can include: detecting a low confidencescenario in the EOG; communicating an EOG calibration request specifyingat least one region in the environment; receiving calibration data inputfrom the region, and updating the environmental object graph with thecalibration data input.

In one example, a calibration request is generated and transmitted to aworker or robotic device. The request preferably specifies a locationand possibly the issue to be calibrated. The worker or robotic devicecan then move to the specified location and collect information that canbe used in enhancing the confidence of the modeling.

In one variation, a resolution event is triggered during low confidencein the accuracy of the EOG's inventory. A low confidence event can bedetected when one or more object instances in the EOG have a datarepresentation indicating low confidence. In one instance, the EOG mayhave low confidence in the state of one particular area of storedinventory.

The resolution event in this instance may involve requesting a worker toinspect the appropriate area of the environment resulting in collectingimage data and/or inventory state data from a region in the environmentidentified in a calibration request. The image data and/or inventorystate data is preferably updated and propagated in the EOG withprioritized confidence levels. In one variation, the worker may manuallyenter inventory state information, which functions to calibrate the EOGsystem. In another variation, a worker or robot may provide image dataon that area through a movable imaging device. In another variation, theresolution event is triggered during low confidence in the itemspossessed by a shopper. Shopper inspection may be applicable infacilitating the checkout process. In the automated checkout embodimentdescribed above, a resolution event may be triggered for a shopper asthey leave the store if a worker needs to verify the possessed items toavoid an incorrect bill. Shopper inspection may alternatively oradditionally be applicable to preventing shoplifting and theft.

Independently or in connection with an EOG calibration request, themethod can include receiving a calibration input at a location. Thecalibration input can be data concerning state of objects in aparticular region. The calibration input can be used in reporting aconfirmed classification or identity of an object, confirming the countof an object, and/or confirming the absence of an object. The updatedobject state information is preferably propagated through the EOG. Thecalibration input can be collected from detailed image data from animage capture device in a mobile capture configuration, from a barcodeor SKU input device, user inputted information. A calibration tool canbe used to collect the calibration input.

In addition to employing workers or movable cameras to provide updatedinput within the environment, a calibration request can additionally beused in obtaining human analysis of the image data. Human workers can beselectively used in facilitating image processing by retrieving humananalysis of media content in managing the EOG. Retrieving human analysiscan include identifying an image processing case for human analysis,transmitting an analysis request and receiving an analysis response.That analysis can then be used in a similar manner as an algorithmicresult. For example, human facilitated object classification can be usedin replacement of CV-driven classification, and a human facilitatedclassification and description of an interaction event can be used inplace of algorithmic and data-driven modeling for those instances.Redundant human analysis requests may be generated and delegated todifferent workers. Additionally, different image processing cases may bedelegated to different workers depending on different factors. Forexample, image analysis related to verifying potential suspiciousbehavior may be delegated to a manager while object classification of ahard to detect object can be delegated to a junior worker. In onevariation, human analysis may be selectively invoked when confidencelevels of objects possessed by a shopper are below a threshold and theshopper is detected approaching a checkout region or when a potentialcheckout event is expected. A worker can preferably receive a batch ofimages and/or video. A particular query or challenge may also bepresented such as “how many cereal boxes does the customer have?” or“What brand of soup was selected”. The worker can then visually reviewthe content and submit human facilitated analysis.

EOG Object Rules and Notifications

The method can additionally support notifications and customizedactions. In one variation, the method can include configuring an objectrule with at least one condition and an action; inspecting the EOG anddetecting occurrence of the object rule; and triggering the action ofthe object rule. This preferably functions to enable the EOG system tofacilitate customized actions. Conditions of object rules can use theproperties of objects to craft a variety of custom rules. Some examplesof conditions can include location fences on particular objects,proximity limits between particular object types, object orientationrules, and object content rules (e.g., what is allowed inside and not).

In one variation, the actions can facilitate programmatic actionswherein object rules can be used in triggering and controlling internalor external service or systems. In one variation, the action can be aconfigured webhook, callback URI, a script, and/or some predefinedaction. At least partial object state information related to the rulecan be communicated with triggering the webhook, callback URI, or scriptsuch that the recipient service or device can use the object stateinformation.

In another variation, object rules can be used in reporting data ortransmitting information at particular times. This can include usage ofa notification system for transmitting communications to a destination.Accordingly, the method can include transmitting notification uponsatisfying an object condition. Notifications can be based on customizedobject rules but could additionally be preconfigured within the system.

EOG Data Storage

The method can additionally include storing bulk media or meta-dataaccording to the EOG to enable meta-media storage. Video anduncompressed data storage can be expensive and resource intensive. TheEOG characterizes object interactions and object relationships within anenvironment, and as such may provide a data efficient representation ofevents and object state in that environment. The EOG can be used tosupplement media and in some cases enabling lower resolution mediastorage or absence of media storage because the individual objectscaptured have object information stored in the EOG. Media records canadditionally be useful in particular events. At any given time and at agiven location within an environment, the EOG will have a varying levelof confidence in its internal representation of the objects in theenvironment. The characteristics of media storage for an imaging devicemay be dynamically adjusted based on the local confidence levelsassociated with the media.

Specifically, the method can include, for a set of media contentsegments, determining a modeling confidence of the EOG, selecting acontent representation based on the modeling confidence; and storing thecontent representation according to the selected media representation asshown in FIG. 27. The content representation is preferably selected froma set of options including at least a modeled data representationassociated with the media representation and a media representation.Preferably, the modeled data representing making up the EOG is stored inboth situations. The content representation may be further adjusted bysetting different characteristics of the media storage. Thecharacteristics of media storage can include the length of storage, theresolution, the frame rate, cropping of the media segment, and/or otheraspects. The length of storage determines how long the mediarepresentation is stored.

When the EOG has higher confidence in an object located within thesurveyed region in a particular media segment then the correspondingmedia may be kept for shorter amounts of time and in reduced mediaformats. When the EOG has lower confidence in an object located withinthe surveyed region in a particular media segment then media storage maybe kept for longer periods of time and/or at enhanced media formats. Forexample, a video camera viewing an unvisited aisle of inventory itemsmay have media stored as simple meta data representation, and a videocamera viewing an aisle where a shopper had an interaction event wherethere is a high level of uncertainty as to what happened may be storedlonger. This allows for subsequent inspection.

In addition to the confidence level of the EOG, media storage may bebased on the objects involved, and in particular the value of theobjects. For example, media capturing a shopper having a suspectinteraction with expensive merchandise may be stored for a longer periodof time than media of a shopper interacting with potatoes. Similarly, ashopper modeled to possess objects that have a high value (monetary orotherwise) may automatically have media stored with higher fidelity andlongevity for media content segments that capture the shopper as theywalk through the environment.

In a similar fashion, media storage may be dynamically managed accordingto person object associations. A shopper can be tracked through anenvironment. When the shopper checks out the method can reconcile thedegree of differences between the associated shopper object in the EOGand the POS itemized receipt. If the contained SKU objects of theshopper object agree with the receipt then storage of the media may bedeprioritized. If the contained SKU objects of the shopper objectconflict with the receipt then storage of the media may be prioritizedbased on degree of the conflict (e.g., number of products, value ofproducts). When used across all shoppers, media associated with noshoppers or shoppers of little concern may be kept for shorter amountsof time or stored in a lower quality format. Media associated withshoppers for which there is some concern may be kept for longer amountsof time and/or in higher quality formats.

4. Method for Checkout

The method for environmental object modeling can be applied to a varietyof scenarios as discussed above. Application of the method to facilitatethe generation of a checkout list for a shopper and optionallyfacilitating execution of a transaction is one exemplary application. Asshown in FIG. 28, the method when applied to checkout list generation ofa preferred embodiment can include collecting image data across anenvironment S410; maintaining an EOG from the image data S420 where theEOG is a data representation of classified objects in space and timeacross the environment, the EOG comprising at least a subset of objectshaving probabilistic object associations; where, in at least oneinstance, maintaining the EOG includes classifying and associating ashopper object and another object in the EOG S430; and inspectingobjects that are probabilistically associated with the shopper objectand thereby generating a checkout list S440. Any of the variationsdescribed herein can similarly be used in combination with the methodapplied to checkout list generation. In particular the method can beused to utilize image data and EOG-based modeling to determine productselection made in one location (e.g., on the shopping floor) that can beacted on in another area. In some variations, actions may be triggeredbased on shopper presence in one region absent of image data confirmingpresence of the object in that region.

Maintaining the environmental object graph will preferably include theiterative execution of the processes described above in maintaining anupdated EOG. In the context of maintaining the EOG as it relates tofacilitating a checkout process, maintaining the environmental objectgraph includes at least one instance of classifying and associating ashopper object and another object. The shopper object can be acustomer/person object, a cart object, a basket classified object, a bagclassified object and/or some collection of shopper associated objects(e.g., a shopper object, various bag objects, a basket object and/or acart object). The other object can be a SKU object (e.g., a producteligible for purchase, renting, or involvement in a transaction).Multiple instances of object-to-shopper associations can be establishedso that a checkout list can be generated that includes multipleproducts.

There are preferably multiple possible EOG modeling scenarios that canbe encountered when maintaining the EOG and thereby impacting thegeneration of a checkout list. Scenarios can involve: various numbers ofobjects and shopper objects; the adding and removal of objectassociations with a shopper object; the changing in confidence levels ofobject classifications and associations; and/or other suitablescenarios.

As shown in FIG. 29, one exemplary instance of maintaining the EOG caninclude: in a first region captured in the image data, classifying afirst object and at least a shopper object; in the first region,detecting an interaction event between the first object and the shopperobject, and updating the environmental object graph whereby the firstobject is probabilistically associated with the shopper object. This canfunction to model a shopper object selecting a product so as to possessor contain that product. These processes are preferably executed when ashopper adds a product to a cart, cart, or their pocket. The firstobject can be probabilistically associated with the shopper object withan initial confidence level; and wherein generating the checkout listcomprises adding objects to the checkout list with confidence levelsatisfying a confidence threshold. Generating the checkout list afterinspecting the EOG can additionally factor in product pricing, customerpurchase history, and/or other factors. For example, the system can havedynamic confidence threshold requirements depending on if the customeris a frequent customer that can be trusted compared to an untrustedcustomer.

Another exemplary instance, maintaining the EOG can be applied so thatthe association of an object to the shopper object can be altered by asubsequent observation. In this scenario, the first object discussedabove is probabilistically associated with the shopper object as apossessed object with a confidence level, and as shown in FIG. 30, themaintaining of the EOG further includes: in a third region captured inthe image data, classifying a second object and the shopper object;associating the second object as a possessed object with the shopperobject with a second confidence level; and updating the EOG wherein thefirst confidence level is altered at least partially in response to thesecond confidence level. This change in confidence level can increase ordecrease confidence of the object. In some cases, the confidence levelcan change such that the dominant classification of the first object canchange. For example, a first object may initially be classified as ageneric box of cereal without association to a particular cereal SKUitem. However, classification of a third object as a particular cerealSKU item in association with the shopper object may result inpropagation of that information such that the first object is modeled asbeing that particular SKU item.

In another exemplary instance, maintaining the EOG can be applied suchthat an object association can be removed or disassociated as a resultof other input to the EOG. As shown in FIG. 31, during a second instanceafter the first instance discussed above, maintaining the EOG caninclude: in the first region, classifying the first object; and updatingthe environmental object graph comprises removing the association of thefirst object with the shopper object in the first instance.

In another exemplary instance, compound object modeling can be used whenmaintaining the EOG such that an object modeled with compound objectassociations to one or more objects can be used to associate containedobjects with a shopper. As shown in FIG. 32, during another instance,maintaining the EOG can include: in a third region captured in the imagedata, classifying an object and the shopper object, wherein the objectis a compound object with probabilistically contained objects; in thethird region, detecting an interaction event between the first objectand the shopper object; and updating the EOG whereby the object and theprobabilistically contained objects of the object are probabilisticallyassociated with the shopper object. For example, a shopper picking up abox of items can have those individual items associated with them andindividually added to a checkout list.

As discussed above there can be various forms of interactions eventsthat may result in association of two or more objects. In one variation,the interaction event can include detecting proximity between an objectand the shopper object satisfying a proximity threshold. The proximitythreshold can vary based on different conditions such as objectclassification, location, shopper history or profile data, and/or othersuitable factors. Interaction events could additionally or alternativelyinclude algorithmically detected gestures or events within the imagedata. Interaction events could similarly be simple object classificationor object tracking changes. For example, if a previously tracked objectdisappears from view without reacquisition, an interaction event can bemodeled that can result in association of that previously tracked objectwith a nearby shopper (without triggering the interaction event based onproximity).

The method of generating a checkout list can be applied in a variety ofapplications such as facilitating a checkout process and/or augmentingoperation of a checkout processing device. In one variation it can besimply used in.

In a variation also shown in FIG. 29, the method can additionallyinclude: in a second region captured in the image data, detecting theshopper object and initiating a checkout process with the checkout list.More preferably, the checkout process and the charged transaction can beautomatically executed by the method further including: accessing anaccount associated with the shopper object; and when initiating thecheckout process, charging the checkout list to the user account afterentering the second region. The account preferably includes a paymentoption such as credit card information, but can alternatively includecredits or have a running tally of billed items. The checkout processmay be initiated independent of explicit observation of productspossessed by the shopper object in that second region. The second regioncan be a particular region of a store. In one variation it can be aregion at the store exits or preceding the store exits.

As with the method above, the method for generating a checkout list canbe used in a system with multiple image capture devices and/or with asystem with a single image capture device. In the automatic checkoutvariation with multiple cameras the first region can be captured inimage data from a first camera and the second region can be captured inimage data from a second camera. Alternatively, the first region and thesecond region can be captured in image data from one camera, which mayenable execution of the method with a single camera. For example, asmall wall of products can be monitored by a camera, and a customercould purchase items from this wall by selecting one and walking away.

In another variation, the method can be used in altering operation of acheckout processing system such as a worker-stationed POS kiosk, aself-checkout POS kiosk, or a customer-facing smart phone applicationused for self-checkout. In a variation used for altering operation of acheckout processing system, initiating the checkout process can includecommunicating the checkout list to a checkout processing system in thesecond region. This can be triggered based on the location of theshopper object as tracked through the EOG relative to the second region.For example, as the shopper approaches a self-checkout kiosk, thecheckout list of that shopper can be communicated to that particularself-checkout kiosk.

The communicated checkout list can be used in a variety of ways. In onevariation, the method can include, at the checkout processing system,populating the checkout processing system with items from the checkoutlist. For example, the self-checkout kiosk in the example above couldautomatically update and present the checkout list on a screen. Theshopper could then confirm and/or supply payment to complete thetransaction. The method could additionally support editing the checkoutlist at the checkout processing system. In some variations, subsets ofthe checkout list can be prepopulated in different modes such as a highconfidence mode and a confirmation mode. Items prepopulated in aconfirmation mode may request user input to confirm or refineidentification of the item. Items added in a high confidence mode may beinitially added without explicit confirmation.

In another variation, the communicated checkout list can be used inbiasing product input of the checkout processing system for items in thecheckout list. This may be used in a variety of ways by the checkoutprocessing system. In one variation, it can alter the navigation of menuoptions for product selection.

The generated checkout list can additionally be used in other scenarios.In one variation it can be used in synchronizing the selected items of ashopper to an application instance of the shopper. A method of thisvariation can include accessing an account associated with the shopperobject; and in an application instance of the account, presenting thecheckout list. Once a checkout list is synchronized and communicated toan application instance, it can be used in various applications. In oneexample, it can enable customers to have a real-time tally of the totalbill prior to checking out an item. In a more specific example, acustomer could simply pick up an item and see the price of that itemreflected on an application on their phone or wearable computer. Thesynchronized checkout list could additionally be used in applicationsrelated to shopping lists, couponing, specialized discounts, and/orother applications.

The systems and methods of the embodiments can be embodied and/orimplemented at least in part as a machine configured to receive acomputer-readable medium storing computer-readable instructions. Theinstructions can be executed by computer-executable componentsintegrated with the application, applet, host, server, network, website,communication service, communication interface,hardware/firmware/software elements of a user computer or mobile device,wristband, smartphone, eyeglasses, wearable device, camera, imagingdevice, or any suitable combination thereof. Other systems and methodsof the embodiment can be embodied and/or implemented at least in part asa machine configured to receive a computer-readable medium storingcomputer-readable instructions. The instructions can be executed bycomputer-executable components integrated by computer-executablecomponents integrated with apparatuses and networks of the typedescribed above. The computer-readable medium can be stored on anysuitable computer readable media such as RAMs, ROMs, flash memory,EEPROMs, optical devices (CD or DVD), hard drives, floppy drives, or anysuitable device. The computer-executable component can be a processor,but any suitable dedicated hardware device can (alternatively oradditionally) execute the instructions.

As a person skilled in the art will recognize from the previous detaileddescription and from the figures and claims, modifications and changescan be made to the embodiments of the invention without departing fromthe scope of this invention as defined in the following claims.

We claim:
 1. A method comprising: collecting image data across anenvironment; maintaining an object graph from the image data wherein theobject graph is a data representation of computer vision classifiedobjects in space and time across the environment, which includesdetecting an interaction event between a product object and a shopperobject, and updating the object graph whereby the product object isassociated with the shopper object; generating, using the object graph,a checkout list from a set of product objects that are associated withthe shopper object.
 2. The method of claim 1, further comprising, in asecond region captured in the image data, detecting the shopper objectand initiating a checkout process with the checkout list.
 3. The methodof claim 2, further comprising accessing an account associated with theshopper object; and wherein initiating the checkout process comprisescharging the checkout list to the account after entering the secondregion.
 4. The method of claim 2, wherein initiating the checkoutprocess comprises communicating the checkout list to a checkoutprocessing system in the second region.
 5. The method of claim 4,wherein communicating the checkout list to the checkout processingsystem further includes, at the checkout processing system, populatingthe checkout processing system with items from the checkout list.
 6. Themethod of claim 5, further comprising biasing product input of thecheckout processing system for items in the checkout list.
 7. The methodof claim 2, wherein detecting an interaction event occurs in a firstregion that is captured in image data from a first camera and the secondregion is captured in image data from a second camera.
 8. The method ofclaim 2, wherein the first region and the second region are captured inimage data from one camera.
 9. The method of claim 1, wherein theproduct object is associated with the shopper object with an initialconfidence level; and wherein generating the checkout list comprisesadding objects to the checkout list with confidence levels satisfying aconfidence threshold.
 10. The method of claim 1, further comprisingaccessing an account associated with the shopper object; and in anapplication instance of the account, presenting the checkout list. 11.The method of claim 1, wherein detecting the interaction event betweenthe product object and the shopper object comprises detecting proximitybetween the product object and the shopper object satisfying a proximitythreshold.
 12. The method of claim 1, wherein maintaining the objectgraph further includes classifying the product object and the shopperobject, which includes comprises applying computer-vision drivenprocesses during classification to determine a product identifier, thecomputer-vision driven processes including at least image featureextraction and classification and an application of neural networks. 13.A method comprising: collecting image data across an environment;maintaining an object graph from the image data whereby maintaining theobject graph is an iterative process comprising: detecting interactionevents of product objects and shopper objects and, for event instancesof a subset of detected interaction events, generating an objectassociation in the object graph of at least one product object and oneshopper object involved in an interaction event instance; and for eachshopper object instance in a set of shopper objects, generating, usingthe object graph, a checkout list from a set of product objects that areassociated with a shopper object instance.
 14. The method of claim 13,wherein detecting the interaction events comprises detecting proximitybetween a product object and a shopper object that satisfies a proximitythreshold.
 15. The method of claim 13, wherein the iterative processfurther comprises classifying objects, which comprises retrievingsupplementary inputs including at least one input selected from the setof: purchase patterns, customer purchase history, and product pricing;and classifying an object at least partially based on one of thesupplementary inputs.
 16. The method of claim 13, further comprising, ina second region captured in the image data, detecting a first shopperobject and initiating a checkout process with a checkout list of thefirst shopper object.
 17. The method of claim 16, further comprisingaccessing an account associated with the first shopper object; andwherein initiating the checkout process comprises charging the checkoutlist to the account after entering the second region.
 18. A systemcomprising: an imaging system configured to collect image data within anenvironment; a processing engine that includes one or more processorsand a non-transitory computer-readable medium that stores an objectgraph based on the image data, the object graph being a datarepresentation of computer vision classified objects in the environment;the non-transitory computer-readable medium storing instructions that,when executed by the one or more processors, cause the processing engineto: detect interaction events of product objects and shopper objectsand, for event instances of a subset of detected interaction events,generate an object association in the object graph of at least oneproduct object and one shopper object involved in an interaction eventinstance, and for each shopper object instance in a set of shopperobjects, generate, using the object graph, a checkout list from a set ofproduct objects that are associated with a shopper object instance. 19.The system of claim 18, further comprising a checkout processing systemwherein the checkout list is communicated to the checkout processingsystem.
 20. The system of claim 18, the instructions, when executed bythe one or more processors, further cause the processing engine tocharge an account associated with a first shopper object for thecheckout list.